TY - JOUR
AU - 
AB - <jats:title>Abstract</jats:title>
               <jats:p>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.</jats:p>
JF - The Oxford Handbook of Political Methodology
DO - 10.1093/oxfordhb/9780199286546.003.0011
DA - 2009-09-02
UR - https://www.deepdyve.com/lp/crossref/2OUDlE4OQ2
SP - 271
EP - 299
DP - DeepDyve
ER -