Statistics and Biostatistics seminar series
Room: M3 3127
Outcome-adaptive LASSO for confounder selection with time-varying treatments
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.