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

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