Causal Inference in Statistics
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 assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
Stats for nerds
5recommend
9mentions
7people
2016–2022years
56% recommend44% neutral
Mentions per year
What Hacker News says
- I really wish that I had something comprehensive to 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 to Statistical Inference,' which sets out the theory very nicely in the first few chapters. That's a starting point at least. There are some interesting courses[^3] that try to teach statistics via resampling that seem pedagogically very valuable. Resampling does build intuition nicely and using it gets people over their squeamishness around using randomized procedures.
[^1]: http://psychology.okstate.edu/faculty/jgrice/psyc5314/Freedm...
[^2]: Sadly really hard to find. We need a cheap reprint of this.
- 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
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- You will benefit from reading Pearl/Glymour/Jewell: Causal Inference in Statistics. A Primer
(The contrast to Imbens and Rubin is crazy)
- FYI there's (at least?) two Judea Pearl books on causality. "Causality" was 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, but usually without proof. It's 40$ on Amazon, strongly recommended. https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...
- 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...
- 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)
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