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A First Course in Causal Inference
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I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only required basic knowledge of probability theory, statistical inference, and linear and logistic regressions.
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Cited by 4 Pith papers
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A formal estimand framework defines treatment effects in platform trials on the entire concurrently eligible population and provides weighting and post-stratification estimators with asymptotic guarantees and efficien...
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A Distributional Perspective on Pearl's Causal Hierarchy: From Marginal to Joint and Individualized Potential Outcomes
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K-Anonymous A/B Testing
OLS regression for A/B tests can be run on k-anonymized aggregate data (counts and sums) and return the same coefficients, enabling privacy-preserving interaction tests and CUPED adjustment.
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