Data sparsity is a common problem when conducting causal inference with time-varying binary treatments, especially when treatment can change over many time-points. Many methods involve weighting by the inverse of the probability of treatment, which requires modeling the probability of treatment at each time point. Under sparsity, it is possible to pool these models over time, but when correlations between covariates and treatment vary over time, this can lead to bias. Furthermore, with a large covariate space assumed to be a non-minimal sufficient adjustment set, reducing the adjustment set can greatly improve the variance of the estimator. We consider a novel approach to longitudinal confounder selection using a longitudinal outcome adaptive fused LASSO that will data-adaptively select covariates and collapse the treatment model parameters over time-points with the goal of improving the efficiency of the estimator while minimizing confounding bias.
Mireille Schnitzer is an Associate Professor of Biostatistics at the Université de Montréal. She holds a Canada Research Chair in Causal Inference and Machine Learning in Health Science. Mireille received her PhD in Biostatistics from McGill University in 2012 and was a postdoctoral researcher at the Harvard T.H. Chan School of Public Health in 2013. Mireille's current research interests are causal inference methodology in pharmacoepidemiology, semiparametric efficient estimation in longitudinal and survival settings with an emphasis on targeted maximum likelihood estimation, and individual participant data meta-analysis. Mireille currently holds an NSERC Discovery Grant and a CIHR Project Grant as PI, and is a co-investigator on multiple CIHR-funded health studies.