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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

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

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.

Game AnalyticsGame DevelopmentHypothesis Proportion

0 likes · 7 min read

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

Model Perspective

Feb 27, 2023 · Fundamentals

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

Learn how the Difference-in-Differences (DiD) method estimates policy impacts by comparing treatment and control groups over time, explore its mathematical model, see a concrete traffic‑restriction example, and follow a step‑by‑step Python implementation with data analysis and visualization.

Pythondifference-in-differenceseconometrics

0 likes · 10 min read

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

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

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

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