Targeted Learning and Variable Importance with Stochastic Interventions
Authors: Nima Hejazi, Jeremy Coyle, and Mark van der Laan
tmle3shift is an adapter/extension R package in the tlverse
ecosystem that exposes support for the estimation of a target parameter
defined as the mean counterfactual outcome under a posited shift of the
natural value of a continuous-valued intervention, using the formalism
of stochastic treatment regimes. As an adapter package, tmle3shift
builds upon the core tlverse grammar introduced by tmle3, a general
framework that supports the implementation of a range of TMLE parameters
through a unified interface. For a detailed description of the target
parameter, TML estimator, and algorithm implemented in tmle3shift, the
interested reader is invited to consult Díaz and van der Laan (2012) and
Díaz and van der Laan (2018). For a general discussion of the framework
of targeted minimum loss-based estimation and the role this methodology
plays in statistical and causal inference, the canonical references are
van der Laan and Rose (2011) and van der Laan and Rose (2018).
Building on the original work surrounding the TML estimator for the
aforementioned target parameter, tmle3shift additionally implements a
set of techniques for variable importance analysis, allowing for a
sequence of mean counterfactual outcomes, estimated under a sequence of
posited shifts, to be summarized via a working marginal structural model
(MSM). The goal of this work is to build upon the tlverse framework
and the estimation methodology implemented for a single mean
counterfactual outcome in order to introduce an end-to-end methodology
for variable importance analyses.
You can install the development version of tmle3shift from GitHub via
devtools with
devtools::install_github("tlverse/tmle3shift")If you encounter any bugs or have any specific feature requests, please file an issue.
- R/
txshift- An R package providing an independent implementation of the TML estimation procedure and statistical methodology as is made available here, without reliance on thetlversegrammar provided bytmle3.
The development of this software was supported in part through a grant from the National Institutes of Health: T32 LM012417-02.
The contents of this repository are distributed under the GPL-3 license.
See file LICENSE for details.
Díaz, Iván, and Mark J van der Laan. 2012. “Population Intervention Causal Effects Based on Stochastic Interventions.” Biometrics 68 (2). Wiley Online Library: 541–49.
———. 2018. “Stochastic Treatment Regimes.” In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, 167–80. Springer Science & Business Media.
van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Science & Business Media.
———. 2018. Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies. Springer Science & Business Media.