The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods

Jasjeet Sekhon

in The Oxford Handbook of Political Methodology

Published in print August 2008 | ISBN: 9780199286546
Published online September 2009 | e-ISBN: 9780191577307 | DOI:

Series: Oxford Handbooks of Political Science

The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods

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This article presents a detailed discussion of the Neyman-Rubin model of causal inference. Additionally, it describes under what conditions ‘matching’ approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability to ensure valid causal inferences. Moreover, the article summarizes Mill's first three canons and shows the importance of taking chance into account and comparing conditional probabilities when chance variations cannot be ignored. The significance of searching for causal mechanisms is often overestimated by political scientists and this sometimes leads to an underestimate of the importance of comparing conditional probabilities. The search for causal mechanisms is probably especially useful when working with observational data. Machine learning algorithms can be used against the matching problem.

Keywords: Neyman-Rubin model; causal inference; matching methods; Mill; causal mechanisms; machine learning algorithms

Article.  14286 words. 

Subjects: Political Methodology ; Comparative Politics

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