Longitudinal efficient adjustment sets for time-varying treatment effect estimation in nonparametric causal graphical models

Abstract: Criteria for identifying optimal adjustment sets (i.e., yielding a consistent estimator with minimal asymptotic variance) for estimating average treatment effects in parametric and nonparametric models have recently been established. In a single treatment time point setting, it has been shown that the optimal adjustment set can be identified based on a causal directed acyclic graph alone. In a longitudinal treatment setting, previous work has established graphical rules to compare the asymptotic variance of estimators based on nested time-dependent adjustment sets. However, these rules do not always permit the identification of an optimal time-dependent adjustment set based on a causal graph alone. In this paper, we extend previous results by exploiting conditional independencies that can be read from the graph. We demonstrate theoretically and empirically that our results can yield estimators with even lower asymptotic variance than those allowed by previous results. We conjecture that our new results may even allow the identification of an optimal time-dependent adjustment set based on the causal graph and provide numerical examples supporting this conjecture.