Doubly Robust Adaptive LASSO for Effect Modifier Discovery

TL;DR

This paper introduces a novel doubly robust adaptive LASSO method for identifying effect modifiers in marginal structural models, improving variable selection and inference in causal effect analysis.

Contribution

It develops a two-stage, data-adaptive approach combining doubly robust estimation with adaptive LASSO for effect modifier discovery in MSMs.

Findings

Effective variable selection demonstrated in simulations

Improved inference accuracy for effect modifiers

Robust performance across different scenarios

Abstract

Effect modification occurs when the effect of the treatment on an outcome differs according to the level of a third variable (the effect modifier, EM). A natural way to assess effect modification is by subgroup analysis or include the interaction terms between the treatment and the covariates in an outcome regression. The latter, however, does not target a parameter of a marginal structural model (MSM) unless a correctly specified outcome model is specified. Our aim is to develop a data-adaptive method to select effect modifying variables in an MSM with a single time point exposure. A two-stage procedure is proposed. First, we estimate the conditional outcome expectation and propensity score and plug these into a doubly robust loss function. Second, we use the adaptive LASSO to select the EMs and estimate MSM coefficients. Post-selection inference is then used to obtain coverage on the selected EMs. Simulations studies are performed in order to verify the performance of the proposed methods.