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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.
This article presents a comprehensive overview of using instrumental variables for causal inference and causal generalization in machine learning, discussing deep learning limitations, Pearl's causal hierarchy, two‑stage regression, challenges with unobserved confounders, automatic IV generation, and applications in economics and social networks.
The paper tackles low-response A/B tests by applying instrumental-variable techniques and optimized propensity-score matching, showing that IV methods recover treatment effects for compliant users and that a refined PSM pipeline dramatically boosts lift detection, turning previously non-significant results into statistically significant business insights.
The article introduces causal inference for observational business data, contrasts methods that require observed confounders with instrumental-variable techniques that can address unobserved confounding, explains the three core IV assumptions plus homogeneity or monotonicity, illustrates the Wald estimator, warns about weak instruments, and urges careful application.
Researchers propose a causal learning framework IV4Rec to separate causal and non-causal factors in recommendations using search data.