Mostly Harmless Econometrics
by Joshua D. Angrist, Jorn-Steffen Pischke · Angrist
An applied econometrics text built around research design: randomized trials, regression, instrumental variables, differences-in-differences, and regression discontinuity. Readers finish able to judge whether an empirical study's identification strategy actually supports its causal claims.
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More resources on Causal Inference & Experiments
mixtape.scunning.com
Mixtape is a teaching resource that compiles concise notes, tutorials, datasets, and runnable code for causal inference and experimental design. It offers practical, hands-on material on randomized trials, quasi-experiments, and evaluation methods with example analyses.
povertyactionlab.org
J-PAL Poverty Action Lab is a global research center that uses randomized controlled trials to evaluate anti-poverty programs. The site provides research results, policy briefs, case studies, and teaching resources on experimental design, data collection, and impact evaluation.
Python Causality Handbook
Matheus Facure's open online book, Causal Inference for the Brave and True, teaching potential outcomes, regression, instrumental variables, difference-in-differences, and matching through runnable Python notebooks. Readers finish able to estimate treatment effects from observational data.
Causal Inference Book (Hernán)
Miguel Hernan and James Robins's textbook Causal Inference: What If, free as a PDF, developing counterfactuals, confounding, standardization, inverse probability weighting, instrumental variables, and g-methods, with accompanying code in R, Python, SAS, and Stata.
Causal Bandits
Causal Bandits Podcast with Alex Molak is here to help you learn about causality, causal AI and causal machine learning through the genius of others. The podcast focuses on causality from a number of different perspectives, finding common grounds between academia and industry, philosophy, theory and practice, and between different schools of thought, and traditions. Your host, Alex Molak is an a machine learning engineer, best-selling author, and an educator who decided to travel the world to record conversations with the most interesting minds in causality to share them with you.Enjoy and stay causal!Keywords: Causal AI, Causal Machine Le...
The Book of Why
Pearl's popular account of the causal revolution in statistics, introducing the ladder of causation, causal diagrams, and do-calculus. Readers come away understanding why correlation-based methods cannot answer intervention and counterfactual questions.