Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Díaz Iván () and J. van der Laan Mark ()
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Díaz Iván: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
J. van der Laan Mark: University of California, Berkeley, CA 94704, USA

The International Journal of Biostatistics, 2013, vol. 9, issue 2, 161-174

Abstract: Assessing the causal effect of an exposure often involves the definition of counterfactual outcomes in a hypothetical world in which the stochastic nature of the exposure is modified. Although stochastic interventions are a powerful tool to measure the causal effect of a realistic intervention that intends to alter the population distribution of an exposure, their importance to answer questions about plausible policy interventions has been obscured by the generalized use of deterministic interventions. In this article, we follow the approach described in Díaz and van der Laan (2012) to define and estimate the effect of an intervention that is expected to cause a truncation in the population distribution of the exposure. The observed data parameter that identifies the causal parameter of interest is established, as well as its efficient influence function under the non-parametric model. Inverse probability of treatment weighted (IPTW), augmented IPTW and targeted minimum loss-based estimators (TMLE) are proposed, their consistency and efficiency properties are determined. An extension to longitudinal data structures is presented and its use is demonstrated with a real data example.

Keywords: causal inference; stochastic interventions; TMLE (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/ijb-2013-0014

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