A First Course in Causal Inference

2023/05/30 by Peng Ding, Ding, Peng · 7 voices · 9 citations
Engineering · Mathematics · Psychology · #Artificial intelligence #Bayesian inference #Causal inference #Computer science #Course (navigation) #Econometrics #Engineering #Fiducial inference #Frequentist inference #Inference #Logistic regression #Mathematics #Mathematics education #Psychology #Statistical inference #Statistics #Statistics Education and Methodologies

paper · pdf · doi:10.48550/arxiv.2305.18793

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2023/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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Discussions

• If you want a sneak peak of the whole book: arxiv.org/abs/2305.18793 I prefer physical copies, but I won't hold it against anyone who prefers electronic versions. [bsky, 37 points, 3 comments]
• More book titles should rank themselves. I teach pretty much the same content as this Peng Ding book, minus a lot of the math, and the masters students say it should be a second course in casual infer [bsky, 18 points, 4 comments]
• I understand that this book written by Peng Ding at UC Berkeley for both undergraduates and graduates is difficult; but I am going to give it a try. arxiv.org/pdf/2305.18793 I'll report on progress fr [bsky, 4 points, 2 comments]
• A First Course in Causal Inference [hn, 2 points, 0 comments]
• via Arthur Charpentier ⏚: "A First Course in Causal Inference" https://arxiv.org/pdf/2305.18793.pdf via https://mastodon.social/@freakonometrics/111361538083143053 [bsky, 2 points, 0 comments]
• do you have reduced form and first-stage at each level of Z? That would be the MR setup arxiv.org/pdf/2305.187... [bsky, 2 points, 1 comments]
• for connections to the causal inference literature, I recommend Peng Ding's excellent textbook highlighting work by Jonathan Hennessy et al and @lmiratrix.bsky.social et al arxiv.org/abs/2305.18793 [bsky, 1 points, 0 comments]

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