Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
This research addresses a gap in causal inference literature for researchers and practitioners dealing with nonlinear treatment-outcome relationships, though it is incremental as it builds upon existing methods like BCF.
This paper tackled the problem of estimating Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) for continuous treatments by introducing a generalized ps-BART model, which consistently outperformed the Bayesian Causal Forest model across three data-generating processes, especially in highly nonlinear settings.
This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables. Across three distinct sets of Data Generating Processes (DGPs), the ps-BART model consistently outperforms the BCF model, particularly in highly nonlinear settings. The ps-BART model's robustness in uncertainty estimation and accuracy in both point-wise and probabilistic estimation demonstrate its utility for real-world applications. This research fills a crucial gap in causal inference literature, providing a tool better suited for nonlinear treatment-outcome relationships and opening avenues for further exploration in the domain of continuous treatment effect estimation.