Controlled direct and mediated effects: definition, identification and bounds

By Tyler J. VanderWeele. 2010

105 citations recorded by the source corpus.

Abstract. Results are given which provide bounds for controlled direct effects when nounmeasured confounding assumptions required for the identification of these effects do not hold. Previous results concerning bounds for controlled direct effects rely on monotonicity relationships between the treatment, mediator and the outcome themselves; the results presented in this article instead assume that monotonicity relationships hold between the unmeasured confounding variable or variables and the treatment, mediator and outcome. Whereas prior results give bounds that contain the null hypothesis of no direct effect, the results presented here will in many instances yield bounds that do not contain the null hypothesis of no direct effect. For contexts in which a set of variables intercepts all paths between a treatment and an outcome, it is possible to provide a definition for a controlled mediated effect. We discuss the identification of these controlled mediated effects; the bounds for controlled direct effects are applicable also to controlled mediated effects. An example is given to illustrate how the results in the article can be used to draw inferences about direct and mediated effects in the presence of unmeasured confounding variables.

Subfields: Economics and Econometrics, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators (2024, methodology): “This abstraction leads us to a simple approach for bias correction, inspired by methods for estimating Controlled Direct Effect (VanderWeele, 2010), for which simple Generalized Linear Models (GLMs) can provide reasonable estimates.”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, methodology): “6 Counterfactual reasoning has been used to develop methods that can address some of the previously described strong assumptions [10-14].”