title: Discussion of Identification, Estimation and Approximation of Risk under Interventions that Depend on the Natural Value of Treatment Using Observational Data, by Jessica Young, Miguel Hernán, and James Robins.
abstract: Young, Hernán, and Robins consider the mean outcome under a dynamic intervention that may rely on the natural value of treatment. They first identify this value with a statistical target parameter, and then show that this statistical target parameter can also be identified with a causal parameter which gives the mean outcome under a stochastic intervention. The authors then describe estimation strategies for these quantities. Here we augment the authors' insightful discussion by sharing our experiences in situations where two causal questions lead to the same statistical estimand, or the newer problem that arises in the study of data adaptive parameters, where two statistical estimands can lead to the same estimation problem. Given a statistical estimation problem, we encourage others to always use a robust estimation framework where the data generating distribution truly belongs to the statistical model. We close with a discussion of a framework which has these properties.
authors.affiliation: Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
authors.affiliation: Division of Biostatistics, University of California, Berkeley, Berkeley, CA, USA, aluedtke@berkeley.edu.
authors.forename: Alexander R
authors.initials: AR
authors.lastname: Luedtke
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA, idiaz@jhu.edu.
authors.forename: Iván
authors.initials: I
authors.lastname: Díaz
keywords: causal inference; dynamic intervention; semi-parametric model; stochastic intervention; targeted learning