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causal inference

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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.

Difference-in-Differencescausal inferenceinstrumental variables
0 likes · 10 min read
Unlocking Everyday Natural Experiments: Design, Examples, and Analysis
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.

Graph Neural NetworksRecommendation systemsWSDM 2025
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
Model Perspective
Model Perspective
Feb 12, 2025 · Fundamentals

Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting

Using a year‑long revenue dataset from an online‑education firm, this article examines how description, causal explanation, and statistical modeling together reveal patterns, uncover underlying drivers, and highlight the limits and uncertainties of forecasting future performance.

business analyticscausal inferencedata analysis
0 likes · 7 min read
Can You Really Predict the Future? Lessons from Data, Causality, and Forecasting
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 GovernanceData Lineage
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 Networksadvertisingcausal inference
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.

Cross-DomainWeak Supervisioncausal inference
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 PlatformsMetric AttributionSHAP
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 GroupRecommendation systemsbackdoor adjustment
0 likes · 16 min read
Causal Debiasing in Ant Group Marketing Recommendation: Data Fusion and Backdoor Adjustment
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 Evaluationcausal inference
0 likes · 10 min read
A/B Testing and Causal Inference: Evolution of Sampling, Metric Evaluation, and Statistical Inference
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
DataFunSummit
DataFunSummit
Jul 13, 2024 · Artificial Intelligence

Causal Inference Knowledge Map: Framework, Application Evaluation, Typical Algorithms, Implementation Challenges, and JD Tech Credit Decision Model

This article presents a comprehensive knowledge map of causal inference covering its overall framework, how to evaluate decision‑making scenarios, typical causal algorithms, practical challenges in deployment, a JD Tech credit‑limit case study, and future research directions.

Decision Modelingalgorithmcausal inference
0 likes · 15 min read
Causal Inference Knowledge Map: Framework, Application Evaluation, Typical Algorithms, Implementation Challenges, and JD Tech Credit Decision Model
DataFunTalk
DataFunTalk
Jul 13, 2024 · Artificial Intelligence

Metric Attribution in Internet Platforms: Concepts, Methods, and Case Studies

This article explains metric attribution for internet platforms, covering its definition, a three‑step framework, basic deterministic and probabilistic methods—including indicator decomposition, machine‑learning and SHAP techniques—illustrated with two detailed case studies and a brief overview of supporting tools.

Internet PlatformsMetric AttributionSHAP
0 likes · 15 min read
Metric Attribution in Internet Platforms: Concepts, Methods, and Case Studies
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.

causal inferencemodel-based RLreinforcement learning
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 NetworkData Mining
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.

GMV predictionadvertisingcausal inference
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.

Difference-in-DifferencesHypothesis Proportioncausal inference
0 likes · 7 min read
Data-Driven Causal Analysis Methods for Game Updates When A/B Testing Is Not Feasible