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Population Intervention Causal Effects Based on Stochastic Interventions
Author(s) -
Muñoz Iván Díaz,
van der Laan Mark
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01685.x
Subject(s) - causal inference , estimator , inverse probability weighting , nonparametric statistics , computer science , inverse probability , marginal structural model , weighting , population , causality (physics) , econometrics , robustness (evolution) , inference , causal model , leverage (statistics) , mathematics , statistics , artificial intelligence , bayesian probability , medicine , environmental health , biochemistry , physics , chemistry , posterior probability , quantum mechanics , gene , radiology
Summary Estimating the causal effect of an intervention on a population typically involves defining parameters in a nonparametric structural equation model (Pearl, 2000, Causality: Models, Reasoning, and Inference ) in which the treatment or exposure is deterministically assigned in a static or dynamic way. We define a new causal parameter that takes into account the fact that intervention policies can result in stochastically assigned exposures. The statistical parameter that identifies the causal parameter of interest is established. Inverse probability of treatment weighting (IPTW), augmented IPTW (A‐IPTW), and targeted maximum likelihood estimators (TMLE) are developed. A simulation study is performed to demonstrate the properties of these estimators, which include the double robustness of the A‐IPTW and the TMLE. An application example using physical activity data is presented.