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Difference-in-Differences

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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
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
Model Perspective
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.

Difference-in-DifferencesPolicy EvaluationPython
0 likes · 10 min read
Mastering Difference-in-Differences: Theory, Example, and Python Implementation
Liulishuo Tech Team
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 testingDifference-in-Differencescausal inference
0 likes · 7 min read
Causal Inference Methods for Quantifying Product Impact in Data Analytics