Causal Inference okładka

Causal Inference

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Causal Inference

Autor: Paul r. Rosenbaum
Czyta: Chris Monteiro
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A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy.

Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce?

Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

©2023 Massachusetts Institute of Technology (P)2023 Ascent Audio
Matematyka Nauka o danych

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the author is well known in the statistical literature as an expert in observational studies, where causality is a key question. this is a very accessible introduction to the potential outcomes framework of causal inference (another popular framework is Pearl's approach based on causal diagrams and do-calculus).

to actually use the techniques, however, you'll probably have to deal with more technical literature. but the big ideas are here.

it is also somewhat difficult to listen to, when the reader reads the formulas. I guess this book is better suited for reading it yourself on paper.

very good introduction to causal inference

Coś poszło nie tak. Spróbuj ponownie za kilka minut.