BestHub
Sign in
Tagged articles

causal inference

180 articles · Page 1 of 2
Data Party THU
Data Party THU
Aug 17, 2026 · Artificial Intelligence

Causal Inference for Text and Image Outcomes: Discovering the Most Affected Feature

This article reviews the paper “Causal Inference with Unstructured Outcomes”, explaining how the authors extend causal analysis from scalar results to text and image data by defining a max‑contrast feature, presenting identification conditions, estimation algorithms, and extensive experiments on formalness, toxicity, image blur, and paired treatment‑result scenarios.

causal inferencegenerative AIimage analysis
0 likes · 14 min read
Causal Inference for Text and Image Outcomes: Discovering the Most Affected Feature
Amap Tech
Amap Tech
Aug 13, 2026 · Artificial Intelligence

Three Amap Papers at CIKM 2026: Route Planning, Generative Search & Recommendation, and Causal Inference

The article showcases three CIKM 2026 papers from Amap—GenMRP for real‑time multi‑route planning, IntSR a deployed generative search‑and‑recommendation system, and FunnelCausalNet for multi‑tier coupon allocation—detailing their motivations, methods, experimental results, and industrial impact.

AmapCIKM 2026causal inference
0 likes · 8 min read
Three Amap Papers at CIKM 2026: Route Planning, Generative Search & Recommendation, and Causal Inference
Woodpecker Software Testing
Woodpecker Software Testing
Aug 10, 2026 · Industry Insights

Shift-Left Testing in 2026: Core Strategies for Quality and Delivery Efficiency

In 2026, as software delivery accelerates and AI-native applications proliferate, shift-left testing has become a strategic necessity, with four emerging trends—requirement testability modeling, AI-driven contract verification, atomic quality gates, and causal-inference-based impact measurement—reshaping how quality is built into development.

AI contract verificationatomic quality gatecausal inference
0 likes · 9 min read
Shift-Left Testing in 2026: Core Strategies for Quality and Delivery Efficiency
DeepHub IMBA
DeepHub IMBA
Jun 12, 2026 · Fundamentals

Spotting Spurious Correlations: Boosting Model Reliability in Real‑World Settings

The article explains the difference between correlation and causation, illustrates three mechanisms that create coincident trends, introduces the Third‑Thing Test for hidden confounders, and offers practical questions to avoid common causal‑mistake traps in data‑driven decision making.

causal inferencecausationconfounding variable
0 likes · 13 min read
Spotting Spurious Correlations: Boosting Model Reliability in Real‑World Settings
Woodpecker Software Testing
Woodpecker Software Testing
Apr 4, 2026 · Product Management

2026 A/B Testing Automation: Emerging Trends and Real‑World Practices

The article examines how 2026’s new A/B testing automation paradigm—combining dynamic traffic allocation, real‑time causal modeling, metric‑autonomy systems, and built‑in privacy compliance—dramatically shortens experiment cycles, boosts statistical power, and transforms experimentation from a manual chore into a scalable, trustworthy decision engine.

A/B testingAutomationGrowth Engineering
0 likes · 8 min read
2026 A/B Testing Automation: Emerging Trends and Real‑World Practices
Bilibili Tech
Bilibili Tech
Feb 13, 2026 · Artificial Intelligence

Self-Forcing: Turning Global Video Diffusion into Causal Streaming for Long-Form Generation

This article examines the Wan2.1 video diffusion model, identifies its scalability bottlenecks for long and real‑time video generation, and introduces the Self‑Forcing causal framework together with sequence‑parallel and RoPE optimizations that achieve sub‑second latency and up to 1.5× speed‑up on modern GPUs.

GPU OptimizationVideo Diffusioncausal inference
0 likes · 14 min read
Self-Forcing: Turning Global Video Diffusion into Causal Streaming for Long-Form Generation
Data Party THU
Data Party THU
Dec 28, 2025 · Artificial Intelligence

How Causal Reinforcement Learning Is Shaping Robust, Explainable AI

This comprehensive survey examines the emerging field of Causal Reinforcement Learning, classifies its core techniques, introduces eleven benchmark environments, evaluates four novel algorithms, and outlines challenges and future research directions for building robust, generalizable, and interpretable AI systems.

AI robustnessExplainabilityalgorithm evaluation
0 likes · 12 min read
How Causal Reinforcement Learning Is Shaping Robust, Explainable AI
Model Perspective
Model Perspective
Dec 6, 2025 · Artificial Intelligence

Understanding the Ladder of Causation: From Correlation to Counterfactuals

Judea Pearl’s Ladder of Causation framework divides reasoning into three levels—association, intervention, and counterfactuals—explaining how conditional probability, the do‑operator, and structural causal models enable moving from mere data correlation to actionable causal insights, with practical criteria like back‑door and front‑door adjustments.

Judea Pearlcausal inferencecounterfactuals
0 likes · 10 min read
Understanding the Ladder of Causation: From Correlation to Counterfactuals
Data Party THU
Data Party THU
Nov 27, 2025 · Artificial Intelligence

Which Python Causal Inference Library Wins? A Hands‑On Comparison of Six Tools

This article compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the U.S. Census Income dataset to answer whether a graduate degree raises the probability of earning over $50K, and provides detailed code, pros, cons, and results for each tool.

BnlearnCausalImpactDoWhy
0 likes · 21 min read
Which Python Causal Inference Library Wins? A Hands‑On Comparison of Six Tools
JD Retail Technology
JD Retail Technology
Nov 20, 2025 · Fundamentals

How Heterogeneous Treatment Effect Analysis Uncovers Sub‑Group Performance

This article explains the concept of heterogeneous treatment effects, outlines how to select dimensions for HTE analysis, describes a Python‑based MVP tool for automated CATE exploration, and showcases a real‑world experiment case where sub‑group insights turned a non‑significant overall result into actionable findings.

CATEcausal inferencedata science
0 likes · 7 min read
How Heterogeneous Treatment Effect Analysis Uncovers Sub‑Group Performance
JD Tech Talk
JD Tech Talk
Nov 20, 2025 · Artificial Intelligence

Unlocking Heterogeneous Treatment Effects: Theory, Methods, and a CATE Tool

This article explains experimental heterogeneity (HTE), clarifies key concepts such as CATE and ITE, discusses why analyzing treatment‑effect variation matters for business, compares statistical and machine‑learning methods, and introduces an open‑source Python tool that automates CATE discovery and reporting.

CATEITEPython
0 likes · 13 min read
Unlocking Heterogeneous Treatment Effects: Theory, Methods, and a CATE Tool
JD Cloud Developers
JD Cloud Developers
Nov 20, 2025 · Artificial Intelligence

How to Reveal Hidden Treatment Effects with Heterogeneous Analysis and CATE Models

This article explains the concept of heterogeneous treatment effects (HTE), clarifies related terminology, outlines why HTE analysis matters for product decisions, and walks through dimension selection, statistical and machine‑learning methods—including ANOVA, causal trees, meta‑learners, and double‑machine‑learning—plus a practical MVP tool with code examples and future development directions.

CATEcausal inferenceexperiment analysis
0 likes · 12 min read
How to Reveal Hidden Treatment Effects with Heterogeneous Analysis and CATE Models
Data Party THU
Data Party THU
Nov 18, 2025 · Artificial Intelligence

Which Python Causal Inference Library Wins? A Deep 5‑Minute Comparison

An in‑depth, five‑minute guide compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the Census Income dataset to illustrate structure learning, parameter estimation, inference, and causal effect validation, highlighting each tool’s strengths, limitations, and ideal use cases.

Bayesian networksCausalImpactDoWhy
0 likes · 21 min read
Which Python Causal Inference Library Wins? A Deep 5‑Minute Comparison
Data STUDIO
Data STUDIO
Nov 11, 2025 · Artificial Intelligence

Which Bayesian Causal Inference Library Best Uncovers Hidden Relationships?

This article systematically compares six popular Python causal inference libraries—Bnlearn, Pgmpy, CausalNex, DoWhy, PyAgrum, and CausalImpact—using the U.S. Census income dataset to demonstrate how each tool discovers causal effects of education on salary, highlighting their core features, strengths, weaknesses, and suitable scenarios.

Bayesian networkBnlearnCausalImpact
0 likes · 22 min read
Which Bayesian Causal Inference Library Best Uncovers Hidden Relationships?
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 8, 2025 · Artificial Intelligence

Time-Series Paper Digest: Nov 1‑7 2025 Highlights

This digest summarizes three recent AI papers—DoFlow, Forecast2Anomaly, and ForecastGAN—detailing their causal generative flow model for interventions, a retrieval‑augmented framework for zero‑shot anomaly prediction, and a decomposition‑based adversarial approach that improves multi‑horizon forecasting across diverse datasets.

anomaly detectioncausal inferencedeep learning
0 likes · 8 min read
Time-Series Paper Digest: Nov 1‑7 2025 Highlights
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 24, 2025 · Artificial Intelligence

Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)

This digest presents seven recent arXiv papers that explore large‑language‑model‑driven portfolio scoring, hybrid ResNet‑RMT covariance denoising for crypto, LLM‑enhanced financial causal analysis, multilingual news alignment for stock returns, three‑step bubble prediction with news and macro data, multimodal volatility forecasting, and news‑aware reinforcement trading, each with reported performance gains.

Financial AILLMcausal inference
0 likes · 15 min read
Weekly AI‑Finance Paper Digest (Oct 18‑24 2025)
Model Perspective
Model Perspective
Sep 22, 2025 · Artificial Intelligence

How Pearl’s Do-Calculus Transforms Causal Inference for Public Health Policies

Pearl’s do‑calculus provides a mathematical framework to derive intervention effects from causal graphs, enabling researchers to predict how policy changes—such as increased vaccination rates—affect disease incidence, with three core rules guiding causal reasoning, substitution, and counterfactual analysis.

Judea PearlPublic Healthcausal graphs
0 likes · 7 min read
How Pearl’s Do-Calculus Transforms Causal Inference for Public Health Policies
DataFunTalk
DataFunTalk
Sep 15, 2025 · Artificial Intelligence

How AI+Data Agents Are Transforming the Automotive Industry’s Digital Leap

In an interview, Di Xingxing of Autohome details their AI+Data framework—unified lake‑warehouse, intelligent engine, and agent services—that breaks data silos, blends traditional models with LLMs, leverages causal inference and RAG knowledge bases, and uses continuous feedback to build explainable, evolving data agents for accurate sales forecasting, competitive analysis, and end‑to‑end business automation in the automotive industry.

AIAutomotiveData Engineering
0 likes · 10 min read
How AI+Data Agents Are Transforming the Automotive Industry’s Digital Leap
Didi Tech
Didi Tech
Jul 31, 2025 · Artificial Intelligence

How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes

This article presents a unified framework for efficiently estimating causal treatment effects on exponential‑family outcomes, extending target regularization beyond Gaussian assumptions, deriving bias analysis for plug‑in estimators, proposing DR and TMLE‑based estimators, and validating them on synthetic and real datasets.

causal inferenceexponential familyneural networks
0 likes · 12 min read
How to Build Efficient Causal Effect Estimators for Exponential‑Family Outcomes
JD Tech
JD Tech
Jul 29, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article describes a QCon talk that combines causal inference with large language models to build a retrieval‑augmented generation pricing system for e‑commerce, detailing the three‑step algorithm, LLM‑driven modeling challenges, process‑reward tree search, reinforcement‑learning fine‑tuning, and experimental gains in accuracy and speed.

Large Language ModelsRetrieval-Augmented Generationcausal inference
0 likes · 17 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
Didi Tech
Didi Tech
Jul 24, 2025 · Artificial Intelligence

How to Estimate Long‑Term Heterogeneous Dose‑Response Curves with Unobserved Confounding

This article presents a data‑fusion framework that combines long‑term observational data and short‑term randomized experiments to identify and estimate long‑term heterogeneous dose‑response curves under continuous treatments and unobserved confounders, using reweighting, optimal transport, and balanced representation learning.

causal inferencedose-responsemachine learning
0 likes · 19 min read
How to Estimate Long‑Term Heterogeneous Dose‑Response Curves with Unobserved Confounding
JD Tech Talk
JD Tech Talk
Jul 23, 2025 · Artificial Intelligence

Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies

This article describes how integrating causal inference with large language models and Retrieval‑Augmented Generation can automate and explain e‑commerce product pricing, detailing the three‑step workflow, reinforcement‑learning rewards, experimental results, and future directions for end‑to‑end RAG‑LLM training.

RAGcausal inferencee‑commerce pricing
0 likes · 15 min read
Causal Inference + LLMs: Transforming E‑Commerce Pricing Strategies
JD Cloud Developers
JD Cloud Developers
Jul 23, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

At QCon 2025, the author presented a novel approach that integrates causal inference with large language models using Retrieval‑Augmented Generation, process rewards, and tree‑search to generate explainable, accurate e‑commerce pricing recommendations, dramatically improving accuracy from 44% to 74% while cutting inference time to seconds.

causal inferencee‑commerce pricingreinforcement learning
0 likes · 14 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
JD Retail Technology
JD Retail Technology
Jul 21, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article presents a comprehensive approach that combines causal inference, large language models, and retrieval‑augmented generation to automate e‑commerce price recommendation, detailing the three‑step workflow, challenges across product categories, the RAG architecture, process‑reward‑guided tree search, reinforcement learning refinements, and experimental results showing significant accuracy and speed improvements.

causal inferencechain-of-thoughte‑commerce pricing
0 likes · 16 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
Didi Tech
Didi Tech
Jul 17, 2025 · Artificial Intelligence

How RAS‑AUCC Eliminates Offline‑Online Gaps in Multi‑Treatment Uplift Modeling

This article explains the challenges of evaluating uplift models for intelligent marketing with multiple discount treatments, reviews existing metrics such as AUUC, Qini, and AUCC, and introduces the RAS‑AUCC metric that aligns offline evaluation with online ROI by sorting samples by marginal ROI and using RCT data.

Marketing OptimizationUplift Modelingcausal inference
0 likes · 13 min read
How RAS‑AUCC Eliminates Offline‑Online Gaps in Multi‑Treatment Uplift Modeling
Instant Consumer Technology Team
Instant Consumer Technology Team
Jul 14, 2025 · Artificial Intelligence

Why Causal Inference Matters: From Theory to Real-World Uplift Models

This article explains the fundamentals of causal inference, distinguishes it from correlation, introduces major theoretical frameworks such as structural causal models and potential outcomes, and demonstrates practical uplift modeling techniques—including meta‑learners, double machine learning, and deep causal networks—through a financial credit‑limit use case.

Uplift Modelingcausal inferencedouble machine learning
0 likes · 17 min read
Why Causal Inference Matters: From Theory to Real-World Uplift Models
Kuaishou Tech
Kuaishou Tech
Jul 7, 2025 · Artificial Intelligence

8 Kuaishou Papers Spotlighted at ICML 2025: Multimodal AI, Causal Inference and More

Kuaishou has had eight cutting‑edge papers accepted at the International Conference on Machine Learning 2025, covering breakthroughs in multimodal emotion modeling, monotonic probability learning, causal effect generalization, cascade ranking, multimodal LLM alignment, ultra‑low‑rate image compression, and visual autoregressive super‑resolution, with links to each work and accompanying code repositories.

AIMultimodalRanking
0 likes · 13 min read
8 Kuaishou Papers Spotlighted at ICML 2025: Multimodal AI, Causal Inference and More
DataFunSummit
DataFunSummit
Jul 5, 2025 · Artificial Intelligence

Automating Causal Subpopulation Mining: Tencent Music’s Experiment Platform Breaks Down the Process

This article explains how Tencent Music’s experiment platform automates strategy‑positive subpopulation mining using unified dimension tables, CATE model training, double‑difference estimation, and propensity‑score matching, enabling rapid recommendation‑strategy optimization and data‑driven product decisions.

CATEExperiment PlatformUplift Modeling
0 likes · 17 min read
Automating Causal Subpopulation Mining: Tencent Music’s Experiment Platform Breaks Down the Process
DataFunSummit
DataFunSummit
Jun 21, 2025 · Artificial Intelligence

From Bias to Fairness: De‑biasing Techniques in Uplift Modeling

This article explores the fundamentals and challenges of uplift modeling, explains why unbiased random data are essential, and presents a comprehensive suite of bias‑correction methods—including reweighting, propensity‑score matching, and advanced deep‑learning architectures such as TarNet, CFRNet, and DragonNet—to improve causal effect estimation in marketing and finance applications.

Bias CorrectionUplift Modelingcausal inference
0 likes · 15 min read
From Bias to Fairness: De‑biasing Techniques in Uplift Modeling
Meituan Technology Team
Meituan Technology Team
Jun 19, 2025 · Fundamentals

How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained

Observational research methods—synthetic control, matching, and Causal Impact—offer powerful alternatives to randomized experiments, enabling businesses like Meituan to assess policy effects despite legal and operational constraints, with detailed principles, applications, advantages, limitations, and practical case studies illustrated throughout the guide.

causal inferencematching methodsobservational study
0 likes · 36 min read
How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained
Meituan Technology Team
Meituan Technology Team
Jun 12, 2025 · Fundamentals

Mastering Difference-in-Differences: From Theory to Meituan’s Real‑World Cases

This article, part of the Trusted Experiment Whitepaper series, introduces quasi‑experimental design and focuses on the Difference‑in‑Differences (DID) method, explaining its principles, evaluation models, parallel‑trend testing, extensions, and a concrete Meituan fulfillment case study illustrating practical implementation.

DIDFixed EffectsParallel Trend
0 likes · 19 min read
Mastering Difference-in-Differences: From Theory to Meituan’s Real‑World Cases
Model Perspective
Model Perspective
May 31, 2025 · Fundamentals

Unlocking Everyday Natural Experiments: Design, Examples, and Analysis

This article explains what natural experiments are, how they differ from controlled trials, and provides practical steps, classic cases, and analytical methods like DID, RDD, and IV to help readers discover and design credible real‑world experiments.

causal inferencedifference-in-differencesinstrumental variables
0 likes · 10 min read
Unlocking Everyday Natural Experiments: Design, Examples, and Analysis
Meituan Technology Team
Meituan Technology Team
May 22, 2025 · Fundamentals

Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments

This article explains the statistical foundations of AB testing, covering the Rubin causal model, SUTVA and randomization assumptions, parameter and confidence‑interval estimation, hypothesis‑testing procedures, and essential limit theorems such as the law of large numbers and the central limit theorem.

AB testingParameter Estimationcausal inference
0 likes · 17 min read
Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments
Meituan Technology Team
Meituan Technology Team
May 22, 2025 · Fundamentals

Why Write an A/B Experiment Whitepaper? – Overview and Methodology

This whitepaper introduces the importance of data‑driven A/B testing, outlines its theoretical foundations, practical challenges such as small samples and spillover effects, and presents a structured roadmap—including experiment basics, statistical principles, advanced designs, and SDK implementation—to help practitioners design trustworthy experiments.

A/B testingcausal inferencedata-driven
0 likes · 18 min read
Why Write an A/B Experiment Whitepaper? – Overview and Methodology
AntTech
AntTech
May 15, 2025 · Artificial Intelligence

Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference

This announcement introduces a live session that will dissect two best‑paper award research works from WSDM 2025—one revealing how recommendation models amplify popularity bias through spectral analysis and proposing a lightweight regularizer, and the other presenting a graph disentangle causal model that integrates GNNs with structural causal models to improve causal inference on networked observational data.

Recommendation SystemsWSDM 2025causal inference
0 likes · 4 min read
Live Deep Dive into Two Award‑Winning WSDM 2025 Papers on Popularity Bias in Recommendation Models and Graph‑Based Causal Inference
DataFunTalk
DataFunTalk
Oct 11, 2024 · Artificial Intelligence

E‑commerce Innovation and Data Governance: Summaries of Recent Research Topics

This article compiles concise overviews of recent e‑commerce research, covering real‑time online learning re‑ranking models, causal inference for user growth, full‑link data lineage, TikTok's data governance and attribution solutions, Volcano Engine's metric management, AI Agent applications on 1688, and XinXuan Group's live‑stream data architecture.

AIData Governancecausal inference
0 likes · 5 min read
E‑commerce Innovation and Data Governance: Summaries of Recent Research Topics
Alimama Tech
Alimama Tech
Sep 11, 2024 · Artificial Intelligence

A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective

The paper introduces a coupled generative adversarial framework that merges biased observational with unbiased experimental data to create a bias‑free dataset for causal inference, enabling robust treatment‑effect estimation under collider bias from an out‑of‑distribution perspective, and demonstrates superior bias reduction on three public advertising datasets.

Generative Adversarial Networkscausal inferenceincremental value
0 likes · 10 min read
A Generative Approach for Treatment Effect Estimation under Collider Bias: From an Out-of-Distribution Perspective
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 11, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments

This article introduces causal inference concepts, explains structural causal and potential‑outcome frameworks, presents the MACR model for debiasing popularity in recommender systems, and details two experiments conducted on the ZhaiZhai platform along with future research directions.

MACRcausal inferencecounterfactual reasoning
0 likes · 13 min read
Causal Inference for Recommender Systems: Fundamentals, the MACR Model, and Practical Experiments
DataFunSummit
DataFunSummit
Sep 11, 2024 · Artificial Intelligence

Weak Supervision Machine Learning in Ant Group Business Scenarios

This article presents an overview of weak supervision machine learning techniques applied to Ant Group’s business scenarios, covering an introduction to weak supervision, challenges of modeling with scarce or noisy labels, detailed methodologies for cross‑domain causal effect estimation, multi‑source noisy label denoising, and real‑world application examples.

Weak Supervisioncausal inferencecross-domain
0 likes · 18 min read
Weak Supervision Machine Learning in Ant Group Business Scenarios
DataFunSummit
DataFunSummit
Sep 10, 2024 · Artificial Intelligence

Automated Social Science with Large Language Models: Framework, Experiments, and Future Outlook

This article presents a comprehensive overview of using large language models to automate the full pipeline of social‑science research—from hypothesis generation and agent construction to experiment execution, data collection, and model estimation—illustrated with a simulated auction study and a discussion of future directions.

agent-based experimentsautomated social sciencecausal inference
0 likes · 8 min read
Automated Social Science with Large Language Models: Framework, Experiments, and Future Outlook
DataFunSummit
DataFunSummit
Sep 3, 2024 · Artificial Intelligence

Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications

This article explains metric attribution for internet platforms, covering its definition, a three‑step analytical framework, deterministic and probabilistic methods such as metric decomposition, machine‑learning models with SHAP values, case studies, and a practical tool that guides users through attribution analysis.

Internet PlatformsSHAPbusiness metrics
0 likes · 15 min read
Metric Attribution on Internet Platforms: Concepts, Methods, and Tool Applications
DataFunSummit
DataFunSummit
Aug 21, 2024 · Artificial Intelligence

Causal Debiasing in Ant Group Marketing Recommendation: Data Fusion and Backdoor Adjustment

This article introduces causal debiasing techniques for Ant Group's marketing recommendation systems, detailing background biases, causal graph analysis, a meta‑learning data‑fusion model (MDI), backdoor‑adjustment methods, extensive experiments on public and internal datasets, and real‑world deployment results.

Ant Groupbackdoor adjustmentcausal inference
0 likes · 16 min read
Causal Debiasing in Ant Group Marketing Recommendation: Data Fusion and Backdoor Adjustment
Data Thinking Notes
Data Thinking Notes
Aug 8, 2024 · Fundamentals

How to Pinpoint the Real Drivers Behind Metric Fluctuations: Methods & Case Studies

This article explains the fundamentals of metric attribution, outlines a three‑step framework for identifying, analyzing, and solving metric changes, compares deterministic, probabilistic, and speculative methods, and illustrates the approach with two real‑world case studies using decomposition and machine‑learning techniques.

Data Analysisbusiness metricscausal inference
0 likes · 16 min read
How to Pinpoint the Real Drivers Behind Metric Fluctuations: Methods & Case Studies
DataFunTalk
DataFunTalk
Jul 22, 2024 · Fundamentals

A/B Testing and Causal Inference: Evolution of Sampling, Metric Evaluation, and Statistical Inference

The article reviews the development of online A/B testing, covering sampling and traffic‑splitting techniques, metric computation improvements, statistical inference advances, and current challenges such as interference, real‑time inference, and large‑scale metric computation, while referencing recent research papers.

A/B testingMetric EvaluationSampling
0 likes · 10 min read
A/B Testing and Causal Inference: Evolution of Sampling, Metric Evaluation, and Statistical Inference
Sohu Tech Products
Sohu Tech Products
Jul 17, 2024 · Artificial Intelligence

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.

AIWeak Supervisioncausal inference
0 likes · 18 min read
How Weak Supervision Powers Ant Group’s Real‑World AI Challenges
DataFunSummit
DataFunSummit
Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 24 min read
Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
DataFunTalk
DataFunTalk
Jul 12, 2024 · Artificial Intelligence

Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications

This article presents a comprehensive overview of weak supervision machine learning techniques applied to Ant Group's business problems, covering theoretical foundations, cross‑domain causal effect estimation, noisy‑label denoising frameworks, experimental results, and practical use cases such as risk modeling and marketing interventions.

Weak Supervisioncausal inferencecross-domain learning
0 likes · 16 min read
Weak Supervision Machine Learning for Ant Group Business Scenarios: Methods, Experiments, and Applications
DataFunSummit
DataFunSummit
Jul 8, 2024 · Artificial Intelligence

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

This article reviews the role of world (mental) models and causal inference in reinforcement learning, covering their theoretical foundations, model‑based RL frameworks such as Dyna, sample‑efficiency challenges, causal structure learning, distribution correction, dynamics‑reward modeling, and experimental results that demonstrate performance gains across multiple tasks.

World Modelscausal inferencemodel-based RL
0 likes · 21 min read
World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview
Qunar Tech Salon
Qunar Tech Salon
Jul 2, 2024 · Artificial Intelligence

Comprehensive Attribution Analysis Methodology and Its Business Application

This article presents a detailed attribution analysis framework—including background research, a four‑step workflow, Bayesian causal detection, Simpson's paradox handling, and real‑world case studies—demonstrating how data‑driven insights can improve conversion rates and operational efficiency across multiple business lines.

Attribution AnalysisBayesian networkSimpson's paradox
0 likes · 15 min read
Comprehensive Attribution Analysis Methodology and Its Business Application
DataFunTalk
DataFunTalk
Jun 24, 2024 · Artificial Intelligence

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

The paper introduces CausalMMM, a variational inference framework that integrates Granger causality and graph neural networks to automatically discover heterogeneous causal structures in marketing mix modeling, enabling more accurate GMV prediction and actionable insights for diverse advertisers.

AdvertisingGMV predictionGraph Neural Network
0 likes · 15 min read
CausalMMM: Learning Causal Structure for Marketing Mix Modeling
DataFunSummit
DataFunSummit
Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingUplift Modelingcausal inference
0 likes · 14 min read
Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice
NetEase LeiHuo UX Big Data Technology
NetEase LeiHuo UX Big Data Technology
Jun 21, 2024 · Game Development

Data-Driven Causal Analysis Methods for Game Updates When A/B Testing Is Not Feasible

When large‑scale A/B testing is impractical for high‑traffic, socially intensive games, developers can rely on methods such as Difference‑in‑Differences, hypothesis proportion analysis, and differential‑ratio comparison to infer the causal impact of content updates on key performance metrics.

Data AnalysisGame AnalyticsGame Development
0 likes · 7 min read
Data-Driven Causal Analysis Methods for Game Updates When A/B Testing Is Not Feasible
DataFunSummit
DataFunSummit
Jun 2, 2024 · Artificial Intelligence

Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases

This article presents a comprehensive overview of building a user profile tag system—including tag taxonomy, platform architecture, construction methods, update cycles, access patterns, common algorithmic tags, and real‑world applications such as marketing, metric attribution, and A/B testing—illustrated with examples and a detailed Q&A session from a data‑mining senior manager at Qunar.

AB testingcausal inferencedata mining
0 likes · 21 min read
Construction and Application of a User Profile Tag System: Methods, Platforms, and Use Cases
DataFunSummit
DataFunSummit
May 25, 2024 · Artificial Intelligence

Debiased Deep Learning and Double Machine Learning for Multi‑Experiment Causal Inference

This article presents a novel approach that combines debiased deep learning with double machine learning to estimate and infer average treatment effects across multiple simultaneous online experiments, detailing problem definition, a semi‑parametric theoretical framework, and extensive field‑experiment validation on a large video‑platform dataset.

ATE estimationcausal inferencedeep learning
0 likes · 11 min read
Debiased Deep Learning and Double Machine Learning for Multi‑Experiment Causal Inference
DataFunSummit
DataFunSummit
May 16, 2024 · Artificial Intelligence

DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection

The DataFun Data Science Summit on May 25 brings together leading experts to present cutting‑edge research on pairwise data causal inference, Retrieval‑Augmented Generation applications, large language model content detection, user growth analytics, and advanced machine‑learning techniques across finance, e‑commerce, and AI domains.

AILLM detectionRetrieval-Augmented Generation
0 likes · 14 min read
DataFun Data Science Summit: Cutting‑Edge Research on Causal Inference, Retrieval‑Augmented Generation, and LLM Content Detection
DataFunSummit
DataFunSummit
May 12, 2024 · Artificial Intelligence

Pairwise Data Based A/B Experiments: Unbiased Causal Inference in Network Experiments

The DataFun Data Science Summit on May 25 will feature Tencent data scientist Li Yilin presenting a comprehensive talk on pairwise‑data A/B experiments, covering unbiased estimation under various randomizations, theoretical analysis, and practical insights for causal inference in network‑driven online experiments.

A/B testingcausal inferencenetwork experiments
0 likes · 4 min read
Pairwise Data Based A/B Experiments: Unbiased Causal Inference in Network Experiments
DataFunTalk
DataFunTalk
May 12, 2024 · Artificial Intelligence

Paired Data Based A/B Experiments: Causal Inference in Network Experiments

The DataFun Data Science Summit on May 25 will feature Tencent data scientist Li Yilin presenting a comprehensive overview of paired‑data A/B experiments, covering causal inference challenges, unbiased estimators under various randomization designs, theoretical analysis, and practical insights for network‑based online experiments.

A/B testingcausal inferencenetwork experiments
0 likes · 5 min read
Paired Data Based A/B Experiments: Causal Inference in Network Experiments
DataFunSummit
DataFunSummit
May 11, 2024 · Artificial Intelligence

Why Causal Inference Matters in Machine Learning and Its Banking Applications

The article explains the necessity of incorporating causal relationships into machine learning, outlines the development of causal science, and details how uplift modeling and causal‑regularized stable learning are applied to marketing and risk control in the banking sector, while also discussing practical challenges and experimental results.

BankingUplift Modelingcausal inference
0 likes · 14 min read
Why Causal Inference Matters in Machine Learning and Its Banking Applications
DataFunSummit
DataFunSummit
May 7, 2024 · Artificial Intelligence

Regional Heterogeneity in Game AB Experiments: Detection, Decomposition, and Prediction

This article examines how game AB experiments can exhibit significant regional differences, outlines a meta‑analysis framework to detect heterogeneity, decomposes its sources into treatment‑effect and distributional factors, and demonstrates how to predict outcomes for unseen regions using machine‑learning models.

AB testingCATEcausal inference
0 likes · 11 min read
Regional Heterogeneity in Game AB Experiments: Detection, Decomposition, and Prediction
DataFunSummit
DataFunSummit
May 1, 2024 · Artificial Intelligence

Causal Solutions for Recommendation System Bias and Practical Applications

This article presents causal inference–based methods to address bias in recommendation systems, covering the transformation of recommendation problems into causal problems, selection bias mitigation through double‑robust and multi‑robust learning, individual treatment effect estimation, and a case study on attention bias in music recommendation.

Bias Mitigationcausal inferencedouble robust learning
0 likes · 12 min read
Causal Solutions for Recommendation System Bias and Practical Applications
Model Perspective
Model Perspective
Apr 18, 2024 · Fundamentals

How Structural Equation Modeling Reveals Hidden Causal Links

Structural Equation Modeling (SEM) combines multiple regression analyses to simultaneously assess direct and indirect relationships among observed and latent variables, offering advantages such as handling multiple causal paths, incorporating latent constructs, flexible error modeling, and testing mediation and moderation effects, illustrated with an education‑investment case study.

causal inferencelatent variablesstatistical methods
0 likes · 9 min read
How Structural Equation Modeling Reveals Hidden Causal Links
Sohu Tech Products
Sohu Tech Products
Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations
DataFunTalk
DataFunTalk
Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
Huolala Tech
Huolala Tech
Mar 28, 2024 · Operations

How Combining Causal Inference with Genetic Algorithms Optimizes Freight Pricing

This article explores a novel framework that merges causal inference with genetic algorithms to improve freight pricing strategies, addressing data limitations, bias, and dynamic market conditions, and demonstrates its robustness and effectiveness through extensive offline simulations and real‑world experiments.

Price Optimizationcausal inferencefreight logistics
0 likes · 23 min read
How Combining Causal Inference with Genetic Algorithms Optimizes Freight Pricing
DataFunSummit
DataFunSummit
Mar 19, 2024 · Artificial Intelligence

Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling

This article explores the challenges of estimating hotel occupancy in online booking platforms and presents four comprehensive approaches—background analysis, demand‑function based quantity‑price modeling, causal‑inference modeling, and multi‑scenario joint modeling—highlighting novel models, datasets, and experimental results for dynamic pricing optimization.

Demand Modelingcausal inferencedynamic pricing
0 likes · 11 min read
Modeling Price-Demand Relationships for Online Hotel Booking: Demand Functions, Causal Inference, and Multi-Scenario Joint Modeling
DataFunTalk
DataFunTalk
Mar 12, 2024 · Artificial Intelligence

Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, including uplift modeling and entropy‑balancing with flexible spatiotemporal grids, to enhance marketing strategy efficiency using observational data while addressing industry‑specific technical challenges.

Marketing OptimizationUplift Modelingcausal inference
0 likes · 10 min read
Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry
DataFunSummit
DataFunSummit
Mar 10, 2024 · Artificial Intelligence

Evaluating Long-Term Strategy Effects with A/B Experiments: Causes, Industry Solutions, and Business Cases

This article examines why A/B experiments often capture only short‑term impacts, explains external and internal factors behind short‑ and long‑term effects, and presents seven industrial methods—including user‑learning models, personalized recommendation adjustments, surrogate metrics, and bias correction—supported by real‑world case studies.

A/B testingBias Correctioncausal inference
0 likes · 14 min read
Evaluating Long-Term Strategy Effects with A/B Experiments: Causes, Industry Solutions, and Business Cases
DataFunTalk
DataFunTalk
Feb 24, 2024 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.

Domain Adaptationcausal discoverycausal inference
0 likes · 11 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
DataFunSummit
DataFunSummit
Feb 14, 2024 · Artificial Intelligence

Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios

This article presents Ant Group's research on causal debiasing for recommendation and marketing, covering the background of bias, common bias types, causal graph analysis, two correction approaches—data‑fusion based MDI and back‑door adjustment based DMBR—along with experimental results on public and proprietary datasets and real‑world deployment insights.

Ant GroupBias Correctioncausal inference
0 likes · 16 min read
Causal Debiasing Methods for Ant Group's Marketing Recommendation Scenarios
DataFunTalk
DataFunTalk
Feb 4, 2024 · Artificial Intelligence

Applying Causal Inference Techniques to Short‑Video Recommendation at Kuaishou

This article presents how causal inference methods are applied to Kuaishou’s single‑column short‑video recommendation, covering the platform’s recommendation scenario, model representations, duration bias mitigation, viewing‑time prediction techniques such as D2Q and TPM, experimental results, and future research directions.

Kuaishoucausal inferenceduration bias
0 likes · 19 min read
Applying Causal Inference Techniques to Short‑Video Recommendation at Kuaishou
DataFunSummit
DataFunSummit
Jan 28, 2024 · Artificial Intelligence

Causal Inference and Bias Correction Methods in Ant Financial Risk Control

This article presents how Ant Group applies causal inference techniques—including confounding bias analysis, double‑difference methods, DiDTree, and shrinkage‑based causal trees—to correct biases in risk‑control scenarios, detailing the theoretical background, algorithmic designs, experimental validation, and practical deployment.

Ant FinancialBias Correctioncausal inference
0 likes · 21 min read
Causal Inference and Bias Correction Methods in Ant Financial Risk Control
Meituan Technology Team
Meituan Technology Team
Jan 25, 2024 · Artificial Intelligence

Design and Implementation of a Distributed Causal Forest Framework on Meituan's Fulfillment Platform

Meituan’s Fulfillment Platform team built a high‑performance distributed causal‑forest framework—named Causal On Spark—that trains hundreds of trees on hundreds of millions of samples within minutes using MapReduce‑based histogram splitting, extensive memory optimizations, Parquet model serving, and novel distributed evaluation metrics, enabling scalable causal inference for pricing, subsidies, and marketing.

Sparkcausal forestcausal inference
0 likes · 23 min read
Design and Implementation of a Distributed Causal Forest Framework on Meituan's Fulfillment Platform
DataFunTalk
DataFunTalk
Jan 25, 2024 · Artificial Intelligence

World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview

This article presents a detailed overview of world models and their role in reinforcement learning, explains how causal inference can enhance model-based RL, discusses sample efficiency challenges, and shares experimental findings and practical insights from recent research and industry applications.

AIcausal inferencemachine learning
0 likes · 22 min read
World Models, Reinforcement Learning, and Causal Inference: A Comprehensive Overview
DataFunSummit
DataFunSummit
Jan 19, 2024 · Fundamentals

Causal Inference and Its Applications in Medical Research

This article reviews the importance of causal inference in medicine, covering historical perspectives on disease causation, epidemiological methods such as Mill's rules and cohort studies, modern techniques like Mendelian randomization, and future research directions in causal graph learning and AI integration.

EpidemiologyMendelian randomizationcausal inference
0 likes · 13 min read
Causal Inference and Its Applications in Medical Research
DataFunTalk
DataFunTalk
Jan 6, 2024 · Artificial Intelligence

Causal Debiasing Techniques for Recommendation and Marketing Scenarios

This article presents Ant Group's causal debiasing techniques for recommendation and marketing, covering bias background, data‑fusion based MDI model, back‑door adjustment methods, experimental results on public and industry datasets, and practical applications in advertising and e‑commerce.

Recommendation Systemscausal inferencedata fusion
0 likes · 16 min read
Causal Debiasing Techniques for Recommendation and Marketing Scenarios
Huolala Tech
Huolala Tech
Jan 5, 2024 · Fundamentals

Unlocking Causal Inference: Practical AB Testing and Observational Study Techniques

This article explains how the Huolala data‑science team tackles AB‑testing challenges, pre‑experiment differences, observational (non‑AB) studies, and advanced causal‑inference methods such as CACE, heterogeneous treatment effects, mediation modeling, regression discontinuity, and instrumental variables to derive reliable business insights.

AB testingcausal inferenceheterogeneous treatment effect
0 likes · 11 min read
Unlocking Causal Inference: Practical AB Testing and Observational Study Techniques
DataFunSummit
DataFunSummit
Dec 26, 2023 · Artificial Intelligence

Applying Causal Inference Tools for Growth Scenarios in Industry

This article explains why causal inference tools are essential for industrial growth, outlines data‑flow standards such as randomized controlled trials, discusses model selection including causal forests and policy learning, and describes evaluation, offline simulation, and resource‑constrained optimization for deploying causal models in production.

AIModel SelectionOptimization
0 likes · 12 min read
Applying Causal Inference Tools for Growth Scenarios in Industry
DataFunSummit
DataFunSummit
Dec 24, 2023 · Artificial Intelligence

Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, especially uplift modeling combined with entropy‑balancing and flexible spatio‑temporal grid partitioning, to enhance marketing strategy efficiency, address confounding bias, and achieve stable, accurate effect estimation across diverse operational scenarios.

AIUplift Modelingcausal inference
0 likes · 10 min read
Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry
DataFunTalk
DataFunTalk
Dec 20, 2023 · Fundamentals

Evaluating Long-Term Effects of Strategies with A/B Experiments: Methods and Case Studies

This article examines why A/B experiments often capture only short‑term impacts, categorises external and internal causes of short‑term bias, and presents seven industry‑tested approaches—including user‑learning models, personalized recommendation adjustments, surrogate metrics, and bias correction techniques—to reliably estimate long‑term strategy effectiveness, illustrated with real business cases.

A/B testingcausal inferenceexperiment design
0 likes · 13 min read
Evaluating Long-Term Effects of Strategies with A/B Experiments: Methods and Case Studies
DataFunTalk
DataFunTalk
Dec 10, 2023 · Operations

Designing Experiments for Peak Surge Pricing in Two‑Sided Markets: Lessons from Uber, Lyft, DoorDash and Didi

This article examines how two‑sided platforms such as Uber, Lyft, DoorDash and Didi design and evaluate peak‑surcharge experiments, addressing network effects, bias‑variance trade‑offs, time‑space slicing, random‑saturation designs, and continuous bandit‑based testing within an operations‑focused experimental system.

AB testingOperationscausal inference
0 likes · 16 min read
Designing Experiments for Peak Surge Pricing in Two‑Sided Markets: Lessons from Uber, Lyft, DoorDash and Didi
DataFunSummit
DataFunSummit
Dec 9, 2023 · Artificial Intelligence

Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery

This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.

Domain Adaptationcausal discoverycausal inference
0 likes · 12 min read
Causal Learning Paradigms: From Prior Causal Structure to Causal Discovery
DataFunSummit
DataFunSummit
Dec 6, 2023 · Artificial Intelligence

Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices

Huya’s data‑driven experiment platform showcases how causal inference, AB testing, and uplift modeling are applied to advertising, user activation, and growth scenarios, detailing platform evolution, metric design, statistical challenges, and practical solutions such as multi‑test correction, CUPED, RTA, and propensity‑score methods.

AB testingExperiment PlatformUplift Modeling
0 likes · 18 min read
Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices
DataFunTalk
DataFunTalk
Nov 28, 2023 · Product Management

Challenges and Technical Solutions for Freight Bilateral Market Experiments

This article examines the unique challenges of conducting experiments in the freight bilateral market—covering transaction, pricing, marketing, and product scenarios—and presents a comprehensive technical solution framework that includes cluster traffic splitting, homogeneity assurance, efficient interpretation, and observational study methods.

bilateral marketcausal inferencefreight
0 likes · 12 min read
Challenges and Technical Solutions for Freight Bilateral Market Experiments
Data Thinking Notes
Data Thinking Notes
Nov 23, 2023 · Big Data

How Data-Driven Metrics Transform Product Analytics and Decision-Making

This article explains how to build a data‑driven metric system—from defining end‑to‑start metrics and combining business and data drivers, to applying statistical analysis, machine‑learning, causal inference, and practical case studies for alerting, diagnosing, and strategizing product performance.

causal inferencedata-drivenmetrics
0 likes · 22 min read
How Data-Driven Metrics Transform Product Analytics and Decision-Making
DataFunSummit
DataFunSummit
Nov 7, 2023 · Artificial Intelligence

Instrumental Variable Based Causal Inference and Generalizable Causal Learning

This article presents a comprehensive overview of using instrumental variables for causal inference and causal generalization in machine learning, discussing deep learning limitations, Pearl's causal hierarchy, two‑stage regression, challenges with unobserved confounders, automatic IV generation, and applications in economics and social networks.

causal inferencecausal learninggeneralization
0 likes · 16 min read
Instrumental Variable Based Causal Inference and Generalizable Causal Learning
DataFunTalk
DataFunTalk
Oct 31, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.

AIGCRecommendation SystemsUplift Modeling
0 likes · 22 min read
Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud
Huolala Tech
Huolala Tech
Oct 27, 2023 · R&D Management

How to Overcome Experimentation Challenges in Freight Two‑Sided Markets

This article examines the unique characteristics of freight two‑sided markets, outlines the experimental challenges across transaction, pricing, marketing, and product scenarios, and presents a comprehensive technical framework—including allocation strategies, homogeneity controls, efficient interpretation, and observational study methods—to achieve reliable, actionable insights.

causal inferencedata scienceexperiment design
0 likes · 12 min read
How to Overcome Experimentation Challenges in Freight Two‑Sided Markets
DataFunSummit
DataFunSummit
Oct 26, 2023 · Big Data

Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases

This article explains how to build and apply a data‑driven metric system, covering end‑to‑end design principles, business‑ versus data‑driven approaches, frameworks such as OSM, GSM and HEART, statistical and machine‑learning techniques, causal inference, and practical case studies that illustrate alerting, diagnosis, and strategy deployment in product operations.

causal inferencedata-drivenmachine learning
0 likes · 21 min read
Data‑Driven Metric System Construction and Application: Theory, Methods, and Real‑World Cases
DataFunSummit
DataFunSummit
Oct 17, 2023 · Artificial Intelligence

DataFunSummit2023: Deep Learning‑Driven Multi‑Experiment Causal Inference and Distributed Causal Tools

The DataFunSummit2023 online conference brings together experts from Tencent and Kuaishou to present cutting‑edge research on causal inference for large‑scale A/B testing, including deep‑learning‑based multi‑experiment effect estimation, a distributed causal inference framework (Fast‑Causal‑Inference), and strategies for evaluating long‑term policy impacts.

A/B testingcausal inferencedata science
0 likes · 7 min read
DataFunSummit2023: Deep Learning‑Driven Multi‑Experiment Causal Inference and Distributed Causal Tools
DataFunSummit
DataFunSummit
Oct 5, 2023 · Artificial Intelligence

Fairness in Recommendation Systems: Consumer and Provider Perspectives

This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.

Recommendation Systemscausal inferenceconsumer perspective
0 likes · 9 min read
Fairness in Recommendation Systems: Consumer and Provider Perspectives
DataFunTalk
DataFunTalk
Oct 4, 2023 · Big Data

Insights into Regional Differences in Overseas A/B Experiments

The presentation explains how to detect, analyze, and leverage regional variations in overseas A/B test results to make more informed product decisions, using a systematic experimental analysis framework grounded in causal inference and online experimentation methods.

A/B testingcausal inferencegame data science
0 likes · 2 min read
Insights into Regional Differences in Overseas A/B Experiments
DataFunTalk
DataFunTalk
Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIBias MitigationRecommendation Systems
0 likes · 3 min read
Research and Product Applications of Causal Inference for Solving Recommendation System Bias
DataFunSummit
DataFunSummit
Sep 3, 2023 · Artificial Intelligence

Estimating Clustered Data Causal Effects with DiConfounder: A Double‑Difference Framework

This article presents a comprehensive approach to estimating causal effects on clustered data using a double‑difference method, introduces the DiConfounder algorithm built on Rubin Causal Model extensions, details data characteristics, model assumptions, six‑step pipeline, and reports competitive results on the ACIC2022 challenge.

DiConfoundercausal inferenceclustered data
0 likes · 13 min read
Estimating Clustered Data Causal Effects with DiConfounder: A Double‑Difference Framework
DataFunSummit
DataFunSummit
Sep 1, 2023 · Artificial Intelligence

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.

Recommendation Systemscausal inferencedeconfounding
0 likes · 22 min read
Observational Causal Inference and De‑Confounding Techniques for Industrial Applications