Massive causal‑inference PDF
Kirk Borne shared a 490‑page free PDF 'A First Course in Causal Inference' alongside Judea Pearl primers — the package was highlighted as ideal deep‑dive material for applying causal methods to finance and policy research. The thread positions the material as foundational for rigorous diff‑in‑diff or IV work (x.com).
Peng Ding’s lecture notes are the source of the free manuscript available on arXiv as arXiv:2305.18793 (v2 revised Oct 3, 2023) and were developed from his UC Berkeley Stat 156/256 course. (arxiv.org) The arXiv/html version organizes material into 29 main chapters plus three appendix chapters and includes numbered homework problems and exercises throughout the text. (ar5iv.labs.arxiv.org) The commercially published edition appears from Chapman & Hall/CRC (Taylor & Francis) with a print release dated July 31, 2024 and a listed hardcover length of 448 pages (ISBN 1032758627). (barnesandnoble.com) Peng Ding and publisher list “all R code and data sets” on Harvard Dataverse, and multiple community Python ports reference the Dataverse dataset DOI 10.7910/DVN/ZX3VEV in their READMEs. (sites.google.com) (github.com) Table of contents and publisher summaries show dedicated chapters on randomized experiments, observational studies, and an explicit chapter on instrumental variables, plus material on propensity scores and doubly‑robust estimators relevant to diff‑in‑diff and IV workflows. (routledge.com) Judea Pearl’s companion primer "Causal Inference in Statistics: A Primer" (Pearl, Glymour, Jewell) remains widely circulated in PDF form and is cited as a compact graphical/structural complement to Ding’s potential‑outcomes/statistical framing. (web.cs.ucla.edu)
