Causality (book)

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Causality: Models, Reasoning, and Inference
Causality 2009 Judea Pearl Book Cover.jpg
Author Judea Pearl
LanguageEnglish
Subject Causality
Publisher Cambridge University Press
Publication date
2000, 2009
Pages484
ISBN 978-0521895606

Causality: Models, Reasoning, and Inference (2000; [1] updated 2009 [2] ) is a book by Judea Pearl. [3] It is an exposition and analysis of causality. [4] [5] It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in several fields including statistics, computer science and epidemiology. [6] In this book, Pearl espouses the Structural Causal Model (SCM) that uses structural equation modeling. [7] This model is a competing viewpoint to the Rubin causal model. Some of the material from the book was reintroduced in the more general-audience targeting The Book of Why.

Contents

Content

The books contents provides a useful summary of the key concept addressed

  1. Introduction to Probabilities, Graphs, and Causal Models
  2. A Theory of Inferred Causation
  3. Causal Diagrams and the Identification of Causal Effects
  4. Actions, Plans, and Direct Effects
  5. Causality and Structural Models in Social Science and Economics
  6. Simpson’s Paradox, Confounding, and Collapsibility
  7. The Logic of Structure-Based Counterfactuals
  8. Imperfect Experiments: Bounding Effects and Counterfactuals
  9. Probability of Causation: Interpretation and Identification
  10. The Actual Cause
  11. Reflections, Elaborations, and Discussions with Readers

Reviews

Pearl succeeds in bringing together in a general nonparametric framework the counterfactual tradition of causal analysis with the variants of structural equation modeling worth keeping. The graph theory that he uses to accomplish this fusion is often elegant. Thus, Causality is a major statement, which all who claim to know what causality is must read.

—  Stephen L. Morgan (2004) [8]

For Hoover (2003, p F412-13) the key idea introduced is what Pearl calls d-separation. This is the view that a set of causes intervening between two variables or a set of parent causes of two variables induces a relationship of conditional independence between them. Through this means Hoover argues, Pearl develop(s) powerful algorithms for inferring causal structure from non-experimental data and for calculating the effects of interventions in one part of the causal structure on other parts (the do-calculus).(2003, p F412-13). For Morgan (2004, p. 413) Pearl's do-operator is his most memorable concept. This is a physical metaphor for ideas normally discussed in the complex language of counterfactuals. And At the risk of oversimplifying, it is not unfair to say that Pearl’s definition of causality rests primarily on this distinction between Pr(Y |X) and Pr(Y | do(X)), thereby bringing the crux of his argument in line with the counterfactual model of causality ( Morgan, 2004, p. 413).

The book earnt Pearl the 2001 Lakatos Award in Philosophy of Science. [9]

In addition to the Morgan (2004) review there have been many other substantial reviews of the book from a range of approaches; the economic, the philosophical and the political. These include:

See also

References

  1. ↑ Judea Pearl (2000). Causality: Models, Reasoning and Inference. Cambridge University Press. ISBN   978-0-521-77362-1. OL   15495862M. Wikidata   Q108441215.
  2. ↑ Judea Pearl (2009). Causality (2nd ed.). New York City: Cambridge University Press. ISBN   978-0-521-89560-6. OCLC   1261134347. OL   27305967M. Wikidata   Q108441243.
  3. ↑ Pearl, Judea (June 18, 2009). "Giving Computers Free Will". Forbes. Archived from the original on January 12, 2020. Retrieved January 12, 2020.
  4. ↑ McArdle, Megan (May 11, 2012). "When Correlation Is Not Causation, But Something Much More Screwy". The Atlantic. Archived from the original on August 18, 2019. Retrieved January 12, 2020.
  5. ↑ Chin, Matthew (July 22, 2016). "Solving big data's 'fusion' problem". UCLA. Archived from the original on October 16, 2019. Retrieved January 12, 2020.
  6. ↑ Porta, Miquel (October 17, 2014). "The deconstruction of paradoxes in epidemiology". OUPblog. Archived from the original on September 26, 2019. Retrieved January 12, 2020.
  7. ↑ Hoover, Kevin D. (2003). "Review of Causality: Models, Reasoning, and Inference". The Economic Journal. 113 (488): F411–F413. ISSN   0013-0133.
  8. ↑ Morgan, Stephen L. "Invited review of Causality: Models, Reasoning, and Evidence by Judea Pearl". Sociological Methods & Research . 32 (2): 411–416. doi:10.1177/0049124104323004. S2CID   220533962.
  9. ↑ Leland Gerson Neuberg (June 6, 2003). "CAUSALITY: MODELS, REASONING, AND INFERENCE, by Judea Pearl, Cambridge University Press, 2000". Econometric Theory . 19 (4). doi:10.1017/S0266466603004109. ISSN   0266-4666. Zbl   1404.00007. Wikidata   Q108441284.
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