Causal Inference in Statistics
by Judea Pearl, Nicholas P. Jewell · first published 2016
71% recommend29% neutral
Why HN recommends it
Concise introduction to causal inference using structural causal models and do-calculus. It is the best introduction to causal questions in statistics, suited to those who already know basic statistics.
Summarised by Claude from recommending comments
Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumpti
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…written in early 2000s, and is filled with theorems + proof + prose detailing all the major results from the theory. It's not a particularly hard book if you studied Mathematics, but it is Mathematics. "Causal inference in statistics" is a 2017 book that covers the actually important results from the previous book, in an approachable manner, and with a focus on applying the results to actual problems. Some theorems are stated formally,…
on “Causal Models” ↗You will benefit from reading Pearl/Glymour/Jewell: Causal Inference in Statistics. A Primer (The contrast to Imbens and Rubin is crazy)
on “Ask HN: What are the best textbooks in your field of expertise?” ↗You might enjoy my blog series on Causality where I work through Pearls 'Causal Inference in Statistics: A Primer' using Python: https://github.com/DataForScience/Causality </ShamelessSelfPromotion>
on “Review: The Book of Why” ↗There's also "The Book of Why", which is a pop-sci-ish book by Pearl and Dana Mackenzie. It contained just enough math and examples to get me really excited for causal inference, so I just bought "Causal inference in statistics" to see the theory in detail. If you want to learn what causal inference is about, but don't necessarily want to wade through a textbook immediately, I highly recommend "The Book of Why". https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...
on “Causal Models” ↗…suggest. There are some things that I think everyone who deals with statistics should read, such as Freedman's 'Statistical Models and Shoe Leather'[^1] and Tukey's 'Exploratory Data Analysis'[^2]. Pearl's 'Causal Inference in Statistics' is the best introduction I know of to issues of causality as we understand them today. For actual inference, the basis is one player game theory (aka, decision theory). I learned it from Kiefer's 'Introduction…
on “We will never have enough software developers (2020)” ↗"Causal Inference in Statistics: A Primer" is somewhat lighter (at least if you have an statistics background). https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...
on “Engines of Evidence – A Conversation with Judea Pearl” ↗I just started reading The Book of Why too, so far so good. I pre-ordered it once I found out it was coming. I've been telling people my view of it is it's like the primer to the primer (Causal Inference in Statistics: A Primer) to a subject-introduction paper (An Introduction to Causal Inference) to the OG math book (Causality). I'm hoping to eventually get back to the nice Causality hardcover I've had on my shelf…
on “How Judea Pearl Became One of AI's Sharpest Critics” ↗…those of you interest in learning more about causality, I just announced on my mailing list (https://data4sci.com/latest) that I'm working on a series of blog posts covering the contents of Judea Pearls's Causal Inference in Statistics - A Primer (https://amzn.to/39G5lWl, affiliate link) using Python.
on “Want to make good business decisions? Learn causality” ↗If you’re interested in learning more about causality, you might be interested in my blog series working through Pearls “Causal Inference in Statistics: A Primer”: https://github.com/DataForScience/Causality (Jupiter notebooks and links to blog posts)
on “The Book of Why: The New Science of Cause and Effect [pdf]” ↗