An Introduction to Causal Inference

by Judea Pearl

This summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.

Genres: Mathematics, Science, Philosophy

94 pages, Kindle Edition

First published April 29, 2014

About the author

Judea Pearl (Hebrew: יהודה פרל) is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks.

Reviews

[A · Rating 2 out of 5]

I bought this book expecting an introduction to causal inference, but instead I found that this is a counterfeit edition of a paper (*) from the International Journal of Biostatistics.

I know close to nothing of Biostatistics, so instead of a gentle description on the subject I was in front of a dry paper where there is a lot of assumed context that I simply didn't have.

Life is too short to read things we don't like to I stopped halfway and moved to the next book. I'm afraid I will need to find a more easy to consume explanation of causal inference somewhere else.


(*) https://econpapers.repec.org/article/...