Causal mediation analysis for stochastic interventions

By Iv'an D'iaz, Nima S. Hejazi. Journal of The Royal Statistical Society Series B-statistical Methodology, 2019

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Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms of direct and indirect effects. We present an analogous decomposition of the population intervention effect, defined through stochastic interventions on the exposure. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart, under the assumption of no mediator–outcome confounders affected by exposure. In particular, identification of direct effects is guaranteed in experiments that randomize the exposure and the mediator. We propose various estimators of the direct and indirect effects, including substitution, reweighted and efficient estimators based on flexible regression techniques, allowing for multivariate mediators. Our efficient estimator is asymptotically linear under a condition requiring n1/4‐consistency of certain regression functions. We perform a simulation study in which we assess the finite sample properties of our proposed estimators. We present the results of an illustrative study where we assess the effect of participation in a sports team on the body mass index among children, using mediators such as exercise habits, daily consumption of snacks and overweight status.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/284a4329808e7af7ca34f828235e451703e4cbc0

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Citations in the corpus

Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning (2020, methodology): “These constructions are inspired by previous work focusing on the bandit and NMDP settings (Díaz and Hejazi, 2020; Muñoz and Van Der Laan, 2012; Haneuse and Rotnitzky, 2013). Definition 1 (Tilting policy). A tilting policy is given by ut : At → R and defined as π t (at | st) = ut(at)π t (at | st)/ ∫”