Average Treatment Effect

Average Treatment Effect (ATE) estimation aims to quantify the causal impact of a treatment or intervention on an outcome of interest, a crucial task across numerous scientific disciplines. Current research emphasizes robust estimation methods that address biases arising from non-randomized studies, focusing on techniques like propensity score matching, doubly robust estimators, and deep learning models to handle complex data structures and confounding variables. These advancements improve the accuracy and reliability of ATE estimates, leading to more informed decision-making in areas such as healthcare, social sciences, and policy evaluation, particularly when dealing with heterogeneous treatment effects and limited data. Furthermore, research is actively exploring methods to handle interference between units and to improve efficiency when outcome data is scarce.