Population Intervention Causal Effects Based on Stochastic Interventions
Article first published online: 6 OCT 2011
© 2011, The International Biometric Society
Volume 68, Issue 2, pages 541–549, June 2012
How to Cite
Muñoz, I. D. and van der Laan, M. (2012), Population Intervention Causal Effects Based on Stochastic Interventions. Biometrics, 68: 541–549. doi: 10.1111/j.1541-0420.2011.01685.x
- Issue published online: 26 JUN 2012
- Article first published online: 6 OCT 2011
- Received February 2011. Revised August 2011., Accepted August 2011.
- Causal effect;
- Counterfactual outcome;
- Double robustness;
- Stochastic intervention;
- Targeted maximum likelihood estimation
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.