Tagged articles

176 articles

Page 2 of 2

Jun 20, 2023 · Artificial Intelligence

Causal Inference–Based Intelligent Diagnosis for E‑Commerce Merchants: Practice and Key Technologies

This article presents a comprehensive overview of applying causal inference to Alibaba’s Business Intelligence platform, detailing fundamental concepts, the merchant intelligent diagnosis system architecture, key technologies such as hybrid causal network discovery (HCM) and deep attribution, and showcases the resulting operational impact.

AIAlibabaKnowledge Graph

0 likes · 14 min read

Causal Inference–Based Intelligent Diagnosis for E‑Commerce Merchants: Practice and Key Technologies

DataFunSummit

Jun 18, 2023 · Artificial Intelligence

Generalized Causal Forest: Construction and Application in Online Trading Markets

This article introduces the generalized causal forest, explains its non‑parametric nonlinear construction for estimating heterogeneous dose‑response functions, compares it with existing methods, and demonstrates its experimental results and deployment in an online ride‑hailing pricing system to balance supply and demand.

Generalized Causal Forestcausal inferenceheterogeneous treatment effect

0 likes · 7 min read

Generalized Causal Forest: Construction and Application in Online Trading Markets

Didi Tech

Jun 12, 2023 · Artificial Intelligence

Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets

Laser (Latent Surrogate Representation learning) estimates long‑term ride‑hailing market effects by inferring hidden surrogate variables from short‑term outcomes using an iVAE and inverse‑probability weighting, thereby reducing experiment cost and latency while achieving more accurate causal effect predictions than existing baselines.

IPWRide HailingUplift Modeling

0 likes · 9 min read

Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets

DataFunTalk

Jun 9, 2023 · Artificial Intelligence

Expert Roundtable on Causal Inference and Large Language Models: Opportunities and Challenges

Leading experts discuss how causal inference intersects with large language models, exploring opportunities, challenges, industry applications, and future research directions, while sharing personal journeys into causal reasoning and offering practical advice for practitioners.

AI researchcausal inferenceexpert interview

0 likes · 16 min read

Expert Roundtable on Causal Inference and Large Language Models: Opportunities and Challenges

DataFunTalk

Jun 1, 2023 · Artificial Intelligence

Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)

This article presents a comprehensive overview of causal inference paradigms, the evolution of uplift and representation‑learning frameworks, and introduces the Mono‑CFR counterfactual credit‑limit model that estimates treatment effects for continuous credit limits using observational data while addressing confounding factors.

AIcausal inferencecounterfactual learning

0 likes · 14 min read

Counterfactual Causal Inference for Credit‑Limit Modeling (Mono‑CFR)

DataFunSummit

May 11, 2023 · Artificial Intelligence

Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices

This article introduces the application of causal inference in financial user operations, outlining typical scenarios such as programmatic advertising and user outreach, discussing data and business challenges, and presenting practical implementations including propensity score matching, sample library construction, experiment design, and full‑stack uplift modeling.

Data ChallengesFinancial MarketingUplift Modeling

0 likes · 14 min read

Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices

DataFunTalk

May 3, 2023 · Artificial Intelligence

Causal Inference for Incentive and Supply‑Demand Optimization in Tencent Weishi

This article presents a comprehensive overview of applying causal inference techniques to Tencent Weishi's cash incentive and video supply‑demand optimization, detailing business modeling, algorithmic frameworks, treatment representations, constrained multivariate causal models, experimental evaluations, and practical deployment insights.

causal inferenceincentive optimizationmachine learning

0 likes · 32 min read

Causal Inference for Incentive and Supply‑Demand Optimization in Tencent Weishi

DataFunTalk

May 1, 2023 · Artificial Intelligence

Trustworthy Intelligent Decision-Making: Framework, Counterfactual Reasoning, Complex Payoffs, Predictive Fairness, and Regulated Decisions

This article presents a comprehensive overview of trustworthy intelligent decision-making, introducing a decision framework and discussing counterfactual reasoning, complex reward modeling, predictive fairness, and regulatory constraints, while highlighting practical methods and recent research advances in each sub‑area.

Fairnesscausal inferencecounterfactual reasoning

0 likes · 29 min read

Trustworthy Intelligent Decision-Making: Framework, Counterfactual Reasoning, Complex Payoffs, Predictive Fairness, and Regulated Decisions

Kuaishou Tech

Apr 25, 2023 · Artificial Intelligence

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

The paper introduces DCCL, a model‑agnostic contrastive learning framework that decouples user interest and conformity representations to address popularity bias and out‑of‑distribution challenges in recommendation systems, demonstrating significant offline and online performance gains on real‑world datasets.

OOD robustnesscausal inferencecontrastive learning

0 likes · 8 min read

DCCL: A Contrastive Learning Framework for Causal Representation Decoupling in Recommendation Systems

DataFunTalk

Apr 24, 2023 · Artificial Intelligence

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

This article reviews the evolution of Weibo's large‑scale recommendation technology, covering the system's business scenarios, technical roadmap, recent large model iterations, multi‑task and multi‑scenario modeling, feature engineering, consistency between recall and ranking, and emerging techniques such as causal inference and graph methods.

Recommendation Systemscausal inferencegraph embeddings

0 likes · 18 min read

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

DataFunSummit

Apr 22, 2023 · Artificial Intelligence

Applying Causal Inference to Limited‑Resource Decision‑Making

This article explains the fundamentals of causal inference, illustrates its distinction from correlation modeling, and demonstrates how causal techniques can be applied to limited‑resource decision problems such as knapsack optimization, ride‑hailing subsidies, and flight pricing, while also covering experimental design, popular models, evaluation metrics, and open challenges.

Decision Optimizationcausal inferenceexperimental design

0 likes · 15 min read

Applying Causal Inference to Limited‑Resource Decision‑Making

DataFunSummit

Apr 6, 2023 · Game Development

Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls

This article presents a data‑science‑focused guide on using causal inference and uplift models to drive overseas ad targeting and user‑operation decisions in games, covering audience selection, privacy‑aware exposure correction, bid optimization, experiment design pitfalls, network effects, and practical recommendations.

A/B testingAdvertisingUplift Modeling

0 likes · 18 min read

Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls

DataFunTalk

Apr 5, 2023 · Artificial Intelligence

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

This article reviews recent developments in causal representation learning, explaining why causal reasoning is essential, describing methods for i.i.d. data, time‑series, and multi‑distribution scenarios, and illustrating applications such as domain adaptation, video analysis, and financial data with numerous examples and visualizations.

causal discoverycausal inferencedomain adaptation

0 likes · 22 min read

Advances in Causal Representation Learning: From i.i.d. to Non‑Stationary Settings

JD Cloud Developers

Feb 27, 2023 · Artificial Intelligence

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

The article explains JD’s Explore & Exploit (EE) module, its bias‑related challenges, the iterative optimization loop, model debiasing techniques for position and popularity bias, personalized bias modeling, causal inference methods, online AB results, and offline evaluation metrics, highlighting significant improvements in search diversity and efficiency.

EE moduleRecommendation Systemsbias mitigation

0 likes · 16 min read

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

Model Perspective

Feb 25, 2023 · Fundamentals

How to Test Causal Relationships with Granger Causality in Python

This article explains the concept of Granger causality, outlines the four possible test outcomes, and provides a step‑by‑step Python implementation using statsmodels to determine whether one time‑series statistically causes another.

Granger causalityPythonStatsmodels

0 likes · 10 min read

How to Test Causal Relationships with Granger Causality in Python

DataFunSummit

Feb 23, 2023 · Artificial Intelligence

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

This article provides a comprehensive overview of causal inference, explaining its definition, the distinction between correlation and causation, classic pitfalls such as Simpson's paradox, key metrics like ATE and ATT, experimental designs, bias mitigation techniques, and practical case studies from content platforms and the Titanic dataset.

A/B testingbias mitigationcausal inference

0 likes · 22 min read

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

DataFunSummit

Feb 5, 2023 · Artificial Intelligence

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

After attending the DataFun causal inference summit, this article outlines why causal analysis matters, its typical use cases, practical challenges, its relationship with A/B testing, and how it integrates with machine learning and deep learning to improve decision‑making and model robustness.

A/B testingDeep LearningUplift Modeling

0 likes · 10 min read

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

DataFunTalk

Jan 29, 2023 · Artificial Intelligence

Data Science Practices in E‑commerce Search: Experimentation, Causal Inference, and Metric Design

This article presents the JD Retail search data‑science team's practical approaches to e‑commerce search, covering the scene’s unique data characteristics, order attribution methods, AB experiment design, causal‑inference frameworks, variance‑reduction techniques, quasi‑experimental evaluations, and metric design for traffic distribution, all illustrated with real‑world examples and visualizations.

Data ScienceMetricscausal inference

0 likes · 18 min read

Data Science Practices in E‑commerce Search: Experimentation, Causal Inference, and Metric Design

DaTaobao Tech

Jan 13, 2023 · Artificial Intelligence

Improving Low-Response AB Experiments via Propensity Score Matching and Instrumental Variable Methods

The paper tackles low-response A/B tests by applying instrumental-variable techniques and optimized propensity-score matching, showing that IV methods recover treatment effects for compliant users and that a refined PSM pipeline dramatically boosts lift detection, turning previously non-significant results into statistically significant business insights.

AB testingPropensity Score Matchingcausal inference

0 likes · 20 min read

Improving Low-Response AB Experiments via Propensity Score Matching and Instrumental Variable Methods

DataFunTalk

Jan 7, 2023 · Artificial Intelligence

How to Better Leverage Data in Causal Inference

This presentation introduces two recent works from Ant Group that improve causal inference by explicitly using historical control data to reduce selection bias and by fusing heterogeneous multi‑source data, describing the GBCT and WMDL methods, their theoretical foundations, experimental results, and practical applications in finance.

Bias Correctioncausal inferencedata fusion

0 likes · 18 min read

How to Better Leverage Data in Causal Inference

DataFunTalk

Dec 26, 2022 · Artificial Intelligence

A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances

This article reviews the two main streams of causal inference—potential‑outcome (Rubin) models and structural causal (Pearl) diagrams—covers classic techniques such as A/B testing, instrumental variables, matching, difference‑in‑differences, synthetic controls, matrix completion, heterogeneous treatment effect estimation, and discusses modern machine‑learning‑based approaches and causal discovery algorithms.

A/B testingcausal inferenceeconometrics

0 likes · 33 min read

A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances

DataFunTalk

Dec 22, 2022 · Artificial Intelligence

Causal Inference: Core Concepts, Differences from Traditional Machine Learning, and Real‑World Applications in Finance

This article introduces the fundamental ideas of causal inference, explains how it differs from correlation‑based machine learning, discusses the role of confounders, and showcases practical implementations in financial services such as offer optimization, uplift modeling, and decision‑making pipelines.

Financial AIUplift Modelingcausal inference

0 likes · 17 min read

Causal Inference: Core Concepts, Differences from Traditional Machine Learning, and Real‑World Applications in Finance

DataFunSummit

Dec 6, 2022 · Artificial Intelligence

Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey

This article surveys the development of artificial intelligence from symbolic and connectionist perspectives, covering deductive and inductive reasoning, multimodal and cross‑modal inference, knowledge‑graph reasoning, text and visual understanding, and their applications in causal inference, dialogue consistency, and security vulnerability analysis.

Knowledge GraphsMultimodal Reasoningcausal inference

0 likes · 18 min read

Multimodal Reasoning, Logic Inference, and Machine Learning: An Integrated Survey

DataFunTalk

Dec 4, 2022 · Artificial Intelligence

Key Insights on Causal Inference: Motivation, Applications, Challenges, and Links to A/B Testing, ML, and Deep Learning

This article summarizes the motivations behind causal inference, its typical business applications such as intelligent decision‑making and prediction, the practical challenges of validation and data, and its relationship with A/B testing, machine learning, and deep learning, providing a concise overview for newcomers.

AB testingBusiness AnalyticsDeep Learning

0 likes · 10 min read

Key Insights on Causal Inference: Motivation, Applications, Challenges, and Links to A/B Testing, ML, and Deep Learning

Bitu Technology

Nov 18, 2022 · Fundamentals

Tubi’s Switchback Experiment Platform: Design, Challenges, and Solutions

The article describes Tubi’s internal experimentation platform, explaining how traditional user‑group A/B tests can suffer from network interference and how Switchback experiments—time‑window based designs—address these issues, detailing their implementation, statistical methods, and the practical challenges overcome.

A/B testingData ScienceSwitchback experiments

0 likes · 12 min read

Tubi’s Switchback Experiment Platform: Design, Challenges, and Solutions

DataFunTalk

Nov 12, 2022 · Artificial Intelligence

Causal Inference Methods for Large‑Scale Game Analytics: Distributed Propensity Score Matching, Robust Double‑Robust Estimation, and Panel DID

This article introduces causal inference methodologies tailored for game scenarios, discusses the challenges of offline inference on massive data, and presents three distributed solutions—low‑complexity propensity‑score matching, robust double‑robust estimation, and panel difference‑in‑differences—along with their implementation details and performance insights.

Game AnalyticsPropensity Score Matchingcausal inference

0 likes · 12 min read

Causal Inference Methods for Large‑Scale Game Analytics: Distributed Propensity Score Matching, Robust Double‑Robust Estimation, and Panel DID

Model Perspective

Nov 8, 2022 · Fundamentals

Why Causal Relationships Matter: From Prediction to Counterfactuals

Understanding why causal relationships matter reveals the limits of predictive machine learning, introduces counterfactual reasoning, explains potential outcomes, treatment effects, bias, and how to distinguish correlation from causation using simple examples like tablet distribution in schools.

Biascausal inferencecounterfactuals

0 likes · 18 min read

Why Causal Relationships Matter: From Prediction to Counterfactuals

Ctrip Technology

Oct 13, 2022 · Fundamentals

Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation

The article explains how causal inference, particularly Propensity Score Matching, can be used to control confounding factors and accurately estimate the incremental value of a marketing campaign when randomized experiments are infeasible, illustrating the method with a real Ctrip project case study.

Propensity Score MatchingValue Estimationcausal inference

0 likes · 15 min read

Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation

Model Perspective

Sep 16, 2022 · Fundamentals

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

This article explains how adding covariates that are not confounders can reduce outcome variance and improve causal inference, while controlling for variables that only predict treatment may introduce selection bias and inflate estimation error.

causal inferencecontrol variablesobservational study

0 likes · 21 min read

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

Model Perspective

Sep 13, 2022 · Fundamentals

Why Linear Regression Is Surprisingly Powerful for Causal Inference

This article explains how linear regression can be used to estimate average causal effects, handle bias, and draw valid conclusions from both randomized experiments and observational data, while illustrating the theory with concrete examples and visualizations.

average treatment effectcausal inferencelinear regression

0 likes · 16 min read

Why Linear Regression Is Surprisingly Powerful for Causal Inference

Model Perspective

Sep 12, 2022 · Fundamentals

Unlocking Causal Reasoning: A Beginner’s Guide to Graphical Models

This article introduces causal graphical models as a language for reasoning about cause‑and‑effect, explains key concepts such as conditional independence, colliders, back‑door paths, confounding and selection bias, and shows how to identify and adjust for bias using simple visual examples.

causal inferencecausal reasoningconfounding

0 likes · 17 min read

Unlocking Causal Reasoning: A Beginner’s Guide to Graphical Models

DataFunSummit

Sep 12, 2022 · Artificial Intelligence

Graph Network Data in Trend‑Driven Video Production on Kuaishou: Concepts, Applications, and Frequency Illusion

This article explains the fundamentals of graph networks, their use in Kuaishou’s short‑video ecosystem for community detection, trend‑driven content creation, and causal inference, and describes how key network nodes and frequency‑illusion effects are leveraged to boost user engagement and content virality.

Kuaishoucausal inferencefrequency illusion

0 likes · 14 min read

Graph Network Data in Trend‑Driven Video Production on Kuaishou: Concepts, Applications, and Frequency Illusion

Model Perspective

Sep 9, 2022 · Fundamentals

How Random Experiments Reveal True Causal Effects in Education

This article explains why randomised experiments are the gold standard for turning correlations into causal claims, illustrates their use in evaluating online versus face‑to‑face learning, and discusses ideal experimental design, assignment mechanisms, and key take‑aways for causal inference.

causal inferenceeducationexperimental design

0 likes · 10 min read

How Random Experiments Reveal True Causal Effects in Education

Model Perspective

Sep 5, 2022 · Fundamentals

Why Understanding Causal Relationships Is Crucial for Machine Learning

This article explains why causal inference matters beyond prediction, introduces potential outcomes notation, demonstrates how bias separates correlation from causation, and outlines the conditions under which observed differences can be interpreted as true causal effects.

BiasPredictioncausal inference

0 likes · 16 min read

Why Understanding Causal Relationships Is Crucial for Machine Learning

网易UEDC

Aug 19, 2022 · Big Data

How Survival Analysis Reveals Player Churn in Naraka: Bladepoint

This article presents a data analyst’s walkthrough of player churn analysis for the battle‑royale game Naraka: Bladepoint, illustrating how survival analysis, epidemiological experiment designs, and econometric causal inference methods can uncover systemic and event‑driven attrition and guide more effective game‑operation strategies.

Game Analyticscausal inferencechurn analysis

0 likes · 8 min read

How Survival Analysis Reveals Player Churn in Naraka: Bladepoint

Python Crawling & Data Mining

Aug 14, 2022 · Product Management

Unlocking Growth: How AB Testing Validates Causality and Measures Impact

This article explains AB testing—from its biomedical origins and online adoption to its types, three essential components, core values of causal validation and quantitative growth, and key characteristics of pre‑evaluation and parallelism—providing a comprehensive guide for data‑driven product optimization.

AB testingcausal inferencedata-driven growth

0 likes · 25 min read

Unlocking Growth: How AB Testing Validates Causality and Measures Impact

Alimama Tech

Aug 3, 2022 · Artificial Intelligence

CausalMTA: Unbiased Multi‑Touch Attribution via Causal Inference

CausalMTA introduces a causal‑inference‑based multi‑touch attribution framework that eliminates static user‑attribute bias through variational recurrent auto‑encoder re‑weighting and dynamic preference bias via a gradient‑reversal‑layer‑augmented RNN, producing an unbiased conversion predictor whose Shapley‑value credits outperform existing methods on synthetic, Criteo, and Alibaba datasets.

Bias CorrectionShapley valuecausal inference

0 likes · 15 min read

CausalMTA: Unbiased Multi‑Touch Attribution via Causal Inference

Continuous Delivery 2.0

Jul 29, 2022 · Product Management

Understanding A/B Testing: Principles, Design, and Decision Making at Netflix

This article explains the fundamentals of A/B testing, how Netflix designs experiments with random sampling and assignment, evaluates metrics to infer causal impact, and uses these insights to guide product decisions and improve member engagement.

A/B testingNetflixcausal inference

0 likes · 11 min read

Understanding A/B Testing: Principles, Design, and Decision Making at Netflix

DataFunTalk

Jun 27, 2022 · Artificial Intelligence

Causal Inference‑Based Attribution Methods in Feizhu Advertising Diagnosis System

This article introduces Feizhu's advertising diagnosis platform and explains how recent causal inference techniques, especially the NO TEARS algorithm and Bayesian‑network‑based attribution, are applied to identify the root causes of performance fluctuations across the ad delivery funnel, improve diagnostic accuracy, and guide optimization decisions.

Ad AttributionAdvertising DiagnosisBayesian network

0 likes · 19 min read

Causal Inference‑Based Attribution Methods in Feizhu Advertising Diagnosis System

DaTaobao Tech

Apr 18, 2022 · Fundamentals

Propensity Score Matching: Principles, Implementation, and Evaluation

The article explains Propensity Score Matching as a causal inference method, detailing treatment effect concepts, required assumptions, score estimation, various matching algorithms, SQL implementation, quality assessment metrics, and how to estimate ATT using Difference-in-Differences, while outlining workflow steps, trade-offs, and alternatives.

Propensity Score MatchingSQLcausal inference

0 likes · 13 min read

Propensity Score Matching: Principles, Implementation, and Evaluation

DaTaobao Tech

Apr 11, 2022 · Industry Insights

How Offline Causal Inference Unlocks 3D Product Value on Taobao

This article explains observational causal inference fundamentals, compares methods like propensity score matching, Bayesian causal graphs, and difference‑in‑differences, and demonstrates their practical application in evaluating the business impact of Taobao's 3D sample rooms.

3d-visualizationBayesian networksPropensity Score Matching

0 likes · 15 min read

How Offline Causal Inference Unlocks 3D Product Value on Taobao

DataFunSummit

Apr 3, 2022 · Artificial Intelligence

Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility

This article explains how Hello Mobility uses tree‑based causal inference and uplift modeling to improve smart subsidy efficiency in hotel marketing, covering background, uplift methods, custom split criteria, offline AUUC evaluation, online deployment, and future research directions.

Marketing OptimizationUplift Modelingcausal inference

0 likes · 17 min read

Tree‑Based Causal Inference for Smart Subsidy Optimization at Hello Mobility

DataFunSummit

Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Propensity Score MatchingRecommendation Systemscausal inference

0 likes · 12 min read

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

DataFunSummit

Mar 17, 2022 · Artificial Intelligence

Optimizing QQ Music Ranking Model: From User Perception to Multi‑Category Traffic Exploration

This talk presents the evolution of QQ Music's ranking system, detailing background challenges, user‑perception modeling, multi‑objective and causal learning to mitigate the Matthew effect, long‑tail content support, cross‑domain recommendation, and module personalization for diversified traffic, concluding with future research directions.

causal inferencecross-domain recommendationmulti-objective learning

0 likes · 16 min read

Optimizing QQ Music Ranking Model: From User Perception to Multi‑Category Traffic Exploration

Meituan Technology Team

Mar 17, 2022 · Artificial Intelligence

Causal Inference for Machine Learning: Paradigms, Differentiable Discovery, and OOD Applications

The article reviews the limitations of association‑based AI, explains the two main causal inference paradigms, introduces differentiable causal discovery, and shows how these ideas address out‑of‑distribution challenges and stable learning in recommendation systems, citing recent research.

causal inferencedifferentiable causal discoveryout-of-distribution recommendation

0 likes · 17 min read

Causal Inference for Machine Learning: Paradigms, Differentiable Discovery, and OOD Applications

DaTaobao Tech

Mar 15, 2022 · Fundamentals

Introduction to Causal Inference and Instrumental Variables

The article introduces causal inference for observational business data, contrasts methods that require observed confounders with instrumental-variable techniques that can address unobserved confounding, explains the three core IV assumptions plus homogeneity or monotonicity, illustrates the Wald estimator, warns about weak instruments, and urges careful application.

Methodologycausal inferenceinstrumental variables

0 likes · 24 min read

Introduction to Causal Inference and Instrumental Variables

DataFunSummit

Mar 10, 2022 · Artificial Intelligence

Applying Causal Inference to Debias Recommendation Systems at Kuaishou

This talk explores how causal inference techniques are used to identify and mitigate various biases in Kuaishou's recommendation pipeline, covering background theory, recent research advances, practical implementations for popularity and video completion debiasing, and reflections on challenges and future directions.

AIKuaishouRecommendation Systems

0 likes · 19 min read

Applying Causal Inference to Debias Recommendation Systems at Kuaishou

HelloTech

Mar 1, 2022 · Artificial Intelligence

Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services

The paper applies causal inference and tree‑based uplift modeling to identify coupon‑responsive riders, using T‑, S‑, and X‑Learners as well as a proprietary Treelift model that directly optimizes per‑user utility, achieving a 4.7% lift over manual rules and 2.3% over prior response models.

AIMarketing OptimizationUplift Modeling

0 likes · 17 min read

Causal Inference and Tree‑Based Uplift Modeling for Intelligent Subsidy in Ride‑Sharing Services

Kuaishou Tech

Feb 24, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how causal inference techniques are applied to identify and correct various biases in Kuaishou's recommendation pipeline, covering background theory, recent research, practical implementations such as popularity debias, causal embedding decoupling, and video completion‑rate debias, along with experimental results and future challenges.

EmbeddingKuaishoubias mitigation

0 likes · 19 min read

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

G7 EasyFlow Tech Circle

Jan 30, 2022 · Artificial Intelligence

Uncovering Road Freight Accident Causes with DoWhy & EconML: A Causal Inference Walkthrough

This article explains why causal inference is essential for decision‑making, contrasts it with pure prediction, outlines the four DoWhy steps (modeling, identification, estimation, refutation), and demonstrates a case study on road freight accidents using DoWhy and EconML with code examples and results.

DoWhyEconMLcausal inference

0 likes · 16 min read

Uncovering Road Freight Accident Causes with DoWhy & EconML: A Causal Inference Walkthrough

DataFunTalk

Dec 30, 2021 · Artificial Intelligence

Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser

This article details Tencent's application of uplift modeling to optimize QQ Browser push notification volume, covering push system characteristics, causal analysis challenges, a refined S‑learner with metric learning, and resulting DAU improvements, while also addressing practical Q&A on uplift techniques.

DAU optimizationPush NotificationTencent

0 likes · 8 min read

Push Notification Volume Optimization Using Uplift Modeling at Tencent Mobile QQ Browser

DataFunSummit

Dec 26, 2021 · Artificial Intelligence

Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies

This article introduces the fundamentals of causal inference with observational data, explains confounding and collider structures, compares observational and experimental approaches, discusses challenges such as Simpson’s paradox, and presents Tencent’s quasi‑experimental applications including DID, regression discontinuity, and uplift modeling.

DIDPropensity Score MatchingQuasi-experiment

0 likes · 26 min read

Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies

DataFunTalk

Dec 6, 2021 · Artificial Intelligence

Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies

This article provides a comprehensive overview of causal inference on observational data, explaining confounding and collider structures, experimental solutions, the differences between observational and experimental data, challenges such as Simpson's paradox, and detailed Tencent case studies using DID, regression discontinuity, and uplift modeling to guide practical analysis.

DIDQuasi-experimentUplift Modeling

0 likes · 26 min read

Observational Data Causal Inference: Fundamentals, Quasi‑Experimental Methods, and Tencent Case Studies

DataFunSummit

Nov 7, 2021 · Artificial Intelligence

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

The article examines how low‑level recommendation‑algorithm improvements in information‑flow feeds can boost user retention, LTV and overall growth by addressing cold‑start challenges, survivor bias, and causal inference through personalized ranking, ecosystem construction, and multi‑task learning.

Information Flowalgorithmcausal inference

0 likes · 14 min read

How Information‑Flow Recommendation Systems Upgrade Drives User Growth

DataFunSummit

Nov 5, 2021 · Artificial Intelligence

Practical Insights into Online Experiment Design and Analysis at Tencent Lookpoint

The presentation offers a comprehensive overview of online experiment fundamentals, design variations, and real-world case studies from Tencent Lookpoint, emphasizing hypothesis validation, causal analysis, best practices, and actionable recommendations for improving product growth and decision‑making.

A/B testingData ScienceRecommendation Systems

0 likes · 20 min read

Practical Insights into Online Experiment Design and Analysis at Tencent Lookpoint

DataFunTalk

Nov 1, 2021 · Product Management

Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform

This article presents a comprehensive overview of online experiment design and analysis, covering basic definitions, AB testing principles, complex experiment types, real-world case studies from Tencent's information flow platform, and practical guidelines for reliable experiment evaluation and product decision‑making.

A/B testingRecommendation Systemscausal inference

0 likes · 21 min read

Online Experiment Design and Analysis: Practices, Case Studies, and Guidelines from Tencent Data Platform

G7 EasyFlow Tech Circle

Sep 15, 2021 · Artificial Intelligence

Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study

Using a road freight accident prediction example, the article warns that interpreting predictive model explanations as causal effects can be misleading, explains when such models may answer causal questions, demonstrates SHAP analysis on an XGBoost model, and recommends causal inference tools like ecoml for reliable effect estimation.

Risk PredictionSHAPXGBoost

0 likes · 10 min read

Can Predictive Models Uncover Causal Effects? A Truck Risk Case Study

DataFunSummit

Sep 5, 2021 · Artificial Intelligence

Causal Inference and Experiment Design in Kuaishou Live Streaming: Methods and Case Studies

This article explains how Kuaishou applies causal inference frameworks, such as Rubin's potential outcomes and Pearl's causal graphs, together with machine‑learning techniques like double‑machine learning, causal forests, and meta‑learners to evaluate product features, recommendation strategies, and user behavior under complex network effects in live streaming.

A/B testingKuaishoucausal inference

0 likes · 14 min read

Causal Inference and Experiment Design in Kuaishou Live Streaming: Methods and Case Studies

Kuaishou Tech

Aug 13, 2021 · Industry Insights

How Kuaishou Uses Causal Inference to Optimize Live‑Streaming Experiments

This article analyzes Kuaishou's live‑streaming ecosystem, detailing causal‑inference frameworks, observational and experimental techniques such as DID, double machine learning, causal forests, uplift meta‑learners, and complex experiment designs like dual‑sided and time‑slice rotation to evaluate product and recommendation strategies.

AB testingKuaishoucausal inference

0 likes · 17 min read

How Kuaishou Uses Causal Inference to Optimize Live‑Streaming Experiments

DataFunTalk

Aug 12, 2021 · Artificial Intelligence

Causal Inference and Experiment Design in Kuaishou Live Streaming

This article presents Dr. Jin Yaran’s comprehensive overview of causal inference challenges, frameworks, and practical case studies—including DID, double machine learning, causal forests, and meta‑learners—applied to Kuaishou’s live‑streaming product, and discusses complex experimental designs such as bilateral and time‑slice experiments.

A/B testingKuaishoucausal inference

0 likes · 15 min read

Causal Inference and Experiment Design in Kuaishou Live Streaming

DataFunSummit

Aug 12, 2021 · Artificial Intelligence

Algorithmic Practices in Hulu’s Video Advertising Platform

This article explains how Hulu leverages machine learning and AI techniques such as ad targeting, inventory prediction, flow matching, conversion rate optimization, causal inference, and shared‑account detection to improve the efficiency, effectiveness, and revenue of its video advertising ecosystem.

AIAd TargetingAdvertising

0 likes · 14 min read

Algorithmic Practices in Hulu’s Video Advertising Platform

DataFunTalk

Jul 29, 2021 · Fundamentals

Offline Sampling in AB Testing: Challenges and Experimental Techniques

The article explains offline sampling for AB testing, detailing why it is needed, the main challenges of limited sample size, population heterogeneity, and non‑random interventions, and presents variance‑reduction, stratified sampling, IPW, and matching methods to address these issues.

AB testingcausal inferenceoffline sampling

0 likes · 15 min read

Offline Sampling in AB Testing: Challenges and Experimental Techniques

Alimama Tech

Jul 28, 2021 · Product Management

Offline Sampling in AB Testing: Challenges and Experimental Techniques

Offline sampling in A/B testing assigns experimental units such as users or tags before a trial begins, but suffers from limited sample size, high heterogeneity, and non‑random allocation, which can be mitigated by variance‑reduction methods like CUPED, stratified sampling with inverse‑probability weighting, and matching approaches including propensity‑score matching.

causal inferenceoffline samplingpropensity score

0 likes · 15 min read

DataFunTalk

Jul 11, 2021 · Fundamentals

Understanding Online Experiments: Origins, Types, and Applications

This article explains the concept, history, and various forms of online experiments such as AB testing, ABn, AA, and multivariate tests, highlighting their role in causal inference, value evaluation, risk control, and product optimization within modern internet businesses.

AB testingcausal inferenceexperiment design

0 likes · 16 min read

Understanding Online Experiments: Origins, Types, and Applications

Alimama Tech

Jul 8, 2021 · Product Management

Understanding Online Experiments: Origins, Development, Types, and Applications

Online experiments, rooted in biomedical randomized controlled trials, have become essential for internet businesses to achieve data‑driven growth by providing causal inference, quantifying value, and managing risk through various designs such as AB, ABn, AA, multivariate and quasi‑experimental tests.

Data-drivencausal inferenceonline experiments

0 likes · 18 min read

Understanding Online Experiments: Origins, Development, Types, and Applications

Didi Tech

May 21, 2021 · Fundamentals

Introduction to Causal Inference and Its Application in Ride‑Hailing Business

The article introduces causal inference for ride‑hailing businesses, explaining the difference between causality and correlation, common misconceptions, and how randomized experiments and observational techniques like propensity‑score matching can quantify effects of actions such as coupons, driver assignments, and platform growth decisions.

Ride Hailingbusiness decisioncausal inference

0 likes · 7 min read

Introduction to Causal Inference and Its Application in Ride‑Hailing Business

DataFunTalk

May 19, 2021 · Artificial Intelligence

Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads

This article explains how causal inference techniques are applied to model the uplift effect of ad placement in Alibaba's Fliggy search CPC advertising, transforming budget allocation into a multi‑objective optimization problem and describing practical control methods, feature engineering, sample re‑sampling, model designs, uplift evaluation, and future research directions.

AdvertisingUplift Modelingbudget allocation

0 likes · 18 min read

Causal Inference for Optimizing Advertising Budget Allocation in Fliggy Search CPC Ads

DataFunTalk

May 13, 2021 · Artificial Intelligence

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

This article introduces the Continuous Causal Forest, a novel uplift modeling approach that expands binary treatment effect estimation to handle multivariate and continuous treatment variables, demonstrates its construction, evaluates its performance on ride‑hailing pricing strategies, and discusses its advantages, limitations, and future research directions.

Pricing strategyUplift Modelingcausal forest

0 likes · 9 min read

Continuous Causal Forest: Extending Uplift Modeling to Multivariate and Continuous Treatments

DataFunTalk

Dec 2, 2020 · Artificial Intelligence

How Recommendation Algorithms Drive User Growth in Content Feed Systems

This article examines how low‑level recommendation algorithm techniques can upgrade content‑feed systems to boost user growth, covering problem analysis, growth factors, personalization upgrades, cold‑start mechanisms, bias mitigation via causal inference, and utility‑driven user profiling.

Recommendation Systemsalgorithm designcausal inference

0 likes · 14 min read

How Recommendation Algorithms Drive User Growth in Content Feed Systems

Liulishuo Tech Team

Oct 26, 2020 · Fundamentals

Causal Inference Methods for Quantifying Product Impact in Data Analytics

This article explains how data analysts can use experimental and observational research methods, including randomized controlled trials, quasi‑experiments, difference‑in‑differences, regression discontinuity, synthetic control, and Bayesian structural time‑series, to assess the causal impact of product and marketing changes on business metrics.

AB testingcausal inferencedifference-in-differences

0 likes · 7 min read

Causal Inference Methods for Quantifying Product Impact in Data Analytics

Beike Product & Technology

Sep 26, 2020 · Artificial Intelligence

Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications

This article introduces uplift (incremental) modeling as a causal inference technique for intelligent marketing, explains its mathematical formulation, compares response and uplift models, describes various modeling approaches such as two‑model, one‑model, and label‑transformation methods, outlines evaluation metrics like Qini and AUUC, and demonstrates practical deployment in a real‑world real‑estate platform.

A/B testingQini curveUplift Modeling

0 likes · 21 min read

Uplift Modeling for Intelligent Marketing: Concepts, Methods, Evaluation, and Business Applications

DataFunTalk

Apr 23, 2020 · Artificial Intelligence

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

The article explains how Alibaba Entertainment leverages causal inference and uplift modeling to build unbiased user‑cf recommendation algorithms that model user states and upgrade personalized distribution, achieving significant click‑through and re‑activation gains for long‑video services like Youku.

Recommendation SystemsVideo platformcausal inference

0 likes · 13 min read

Causal Inference–Based Recommendation Algorithms for User Growth in Video Platforms

Youku Technology

Apr 2, 2020 · Artificial Intelligence

In‑Depth Overview of Intelligent Marketing Uplift Modeling

The talk explains uplift modeling for intelligent marketing, showing how causal lift predictions—derived from randomized experiments using two‑model, one‑model, or tree‑based methods—identify truly responsive users, evaluate performance with AUUC/Qini, and were applied to Taopiaopiao’s coupon allocation via knapsack optimization, highlighting challenges and future directions.

A/B testingUplift Modelingcausal inference

0 likes · 16 min read

In‑Depth Overview of Intelligent Marketing Uplift Modeling

DataFunTalk

Mar 27, 2020 · Artificial Intelligence

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

The article explains how data products create business value through application, data, and algorithm layers, using examples like 5G infrared temperature screening and ImageNet, and discusses the roles of experimental design, causal inference, and reinforcement learning in building effective AI‑driven strategies.

Data Productartificial intelligencebusiness value

0 likes · 8 min read

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

DataFunTalk

Mar 3, 2020 · Artificial Intelligence

Causal Inference Guided Stable Learning: Improving Explainability and Prediction Stability in Machine Learning

Machine learning models often suffer from poor explainability and unstable predictions due to reliance on spurious correlations, but by applying causal inference to separate true causal relationships from confounding and selection bias, a causal‑constrained stable learning framework can achieve more interpretable and robust predictions across varying data distributions.

causal inferenceexplainabilitymachine learning

0 likes · 14 min read

Causal Inference Guided Stable Learning: Improving Explainability and Prediction Stability in Machine Learning

DataFunTalk

Aug 19, 2019 · Artificial Intelligence

Algorithmic Practices in Hulu's Video Advertising System

This article details how Hulu leverages machine learning and AI techniques—including ad targeting, inventory prediction, conversion rate optimization, causal inference, and real‑time bidding—to improve ad efficiency, user experience, and revenue across its video streaming platform.

AIAd TargetingAdvertising

0 likes · 15 min read

Algorithmic Practices in Hulu's Video Advertising System