Estimation of a Semiparametric Natural Direct Effect Model Incorporating Baseline Covariates
By E. T. Tchetgen Tchetgen, Ilya Shpitser. 2014
33 citations recorded by the source corpus.
SUMMARY Establishing cause-effect relationships is a standard goal of empirical science. Once the presence of a causal relationship is established, the precise causal mechanism involved becomes a topic of interest. A particularly popular type of mechanism analysis concerns questions of mediation, that is to what extent an effect is direct, and to what extent it is mediated by a third variable. A semiparametric theory has recently been proposed which allows multiply robust estimation of direct and mediated marginal effect functionals in observational studies (Tchetgen Tchetgen & Shpitser, 2012). In this paper we extend the new theory to handle parametric models of natural direct and indirect effects within levels of pre-exposure variables with an identity or log link function, where the model for the observed data likelihood is otherwise unrestricted. We show that estimation is generally not feasible in this model because of the curse of dimensionality associated with the required estimation of auxiliary conditional densities or expectations, given high-dimensional covariates. Thus, we consider multiply robust estimation and propose a more general model which assumes that a subset but not all of several working models holds.
Subfields: Economics and Econometrics, Statistics and Probability
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In Economics and Econometrics
In Statistics and Probability
- Ilya Shpitser
- E. T. Tchetgen Tchetgen
- Caleb H. Miles
- Phyllis Kanki
- Seema Meloni
- Razieh Nabi
- Edward H. Kennedy
- Judea Pearl
Citations in the corpus
- On Partial Identification of the Pure Direct Effect (2015, background): “While some work has been done to develop sensitivity analyses for unmeasured confounding of the mediator (Tchetgen Tchetgen, 2011; Tchetgen Tchetgen and Shpitser, 2012; Vansteelandt and VanderWeele, 2012), sensitivity analyses for ranges of plausible associations between cross-world counterfactuals ”
- CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS (2014, methodology): “This was extended to the semi-parametric models for direct effects on the additive and multiplicative scales [32].”