quasi-experiment

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.

DIDSimpson's paradoxcausal inference

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.

DIDMachine Learningcausal inference

0 likes · 26 min read

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