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An optimization approach for making causal inferences
Author(s) -
Tam Cho Wendy K.,
Sauppe Jason J.,
Nikolaev Alexander G.,
Jacobson Sheldon H.,
Sewell Edward C.
Publication year - 2013
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12004
Subject(s) - covariate , causal inference , matching (statistics) , observational study , set (abstract data type) , selection (genetic algorithm) , key (lock) , control (management) , computer science , group (periodic table) , mathematics , mathematical optimization , econometrics , machine learning , artificial intelligence , statistics , chemistry , computer security , organic chemistry , programming language
To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework.