Doubly robust adaptive LASSO for effect modifier discovery.

Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.

Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators.

Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.

Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis.

This paper investigates different approaches for causal estimation under multiple concurrent medications. Our parameter of interest is the marginal mean counterfactual outcome under different combinations of medications. We explore parametric and non-parametric methods to estimate the generalized propensity score. We then apply three causal estimation approaches (inverse probability of treatment weighting, propensity score adjustment, and targeted maximum likelihood estimation) to estimate the causal parameter of interest. Focusing on the estimation of the expected outcome under the most prevalent regimens, we compare the results obtained using these methods in a simulation study with four potentially concurrent medications. We perform a second simulation study in which some combinations of medications may occur rarely or not occur at all in the dataset. Finally, we apply the methods explored to contrast the probability of patient treatment success for the most prevalent regimens of antimicrobial agents for patients with multidrug-resistant pulmonary tuberculosis.