Python Causality Handbook
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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.
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.
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...
Causal Inference
This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course.
causalinference.gitlab.io
A GitLab Pages site with educational content on causal inference and experimental design, featuring tutorials, lecture notes, and reference materials on topics such as randomized experiments, observational causal methods, potential outcomes, and causal diagrams.