Efficient Adjustment Sets for Estimating Time-Varying Treatment Effects in Nonparametric Causal Graphical Models

Extending previous results, this paper proposes a novel definition of sufficient time-dependent adjustment sets that can yield estimators with lower asymptotic variance compared to existing methods, by exploiting conditional independencies in the causal graph.