Population Intervention Causal Effects Based on Stochastic Interventions.
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.
Ivan Diaz and Mark van der Laan. "Population Intervention Causal Effects Based on Stochastic Interventions." Biometrics (2012).