A First Course in Causal Inference

2023/05/30 by Peng Ding, Ding, Peng · 7 voices · 7 citations
Mathematics · #Statistics Education and Methodologies

paper · pdf · doi:10.48550/arxiv.2305.18793

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.

Cited by

• Design-based finite-sample analysis for regression adjustment
• From Structural Equation Modeling to Targeted Learning: A Tutorial Introduction to Targeted Maximum Likelihood Estimation for SEM Researchers
• A Distributional Perspective on Pearl's Causal Hierarchy: From Marginal to Joint and Individualized Potential Outcomes
• Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
• A Tutorial on Conducting Mediation Analysis with Exposure Mixtures
• Optimal Inference of the Mean Outcome under Optimal Treatment Regime
• Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions

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]

Related

• A Brief Introduction to Causal Inference in Machine Learning
• Causality and Statistical Learning
• Three Lectures on Probability and Statistics
• Probability and Statistical Inference
• Methods of Causal Inference in Physics Education Research