Population Intervention Causal Effects Based on Stochastic Interventions Academic Article uri icon

Overview

MeSH Major

  • Biometry
  • Causality
  • Stochastic Processes

abstract

  • 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.

publication date

  • June 2012

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4117410

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2011.01685.x

PubMed ID

  • 21977966

Additional Document Info

start page

  • 541

end page

  • 9

volume

  • 68

number

  • 2