0 views collected around this technical thread.
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.
Drawing inspiration from Robert Frost’s poem, this article explains the challenges of causal inference in social sciences, contrasts randomized experiments with observational methods, and introduces key techniques such as propensity score matching, instrumental variables, and regression discontinuity designs for estimating causal effects without randomization.
Regression Discontinuity Design (RDD) leverages a predefined cutoff to compare individuals on either side, mimicking random assignment and allowing researchers to infer causal effects when randomized experiments are infeasible, with applications ranging from education scholarships to tax policies.
This article introduces the fundamental concepts and theoretical frameworks of causal inference, explains Rubin's potential outcomes and Pearl's causal graph models, and demonstrates their practical deployment through uplift modeling, propensity‑score matching, synthetic control, and regression‑discontinuity case studies within Ctrip's train ticket business.
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.