Ablation Studies for Novel Treatment Effect Estimation Models

TL;DR

This paper investigates the role of ablation studies in treatment effect estimation models, specifically analyzing the Bayesian Causal Forest, and finds that excluding the propensity score does not impair performance but improves efficiency.

Contribution

It provides the first detailed ablation analysis of the Bayesian Causal Forest model, highlighting that certain components may be unnecessary for effective treatment effect estimation.

Findings

Excluding the propensity score does not reduce estimation accuracy.

Omitting the propensity score decreases computational time by about 21%.

Ablation studies are crucial for understanding model component importance.

Abstract

Ablation studies are essential for understanding the contribution of individual components within complex models, yet their application in nonparametric treatment effect estimation remains limited. This paper emphasizes the importance of ablation studies by examining the Bayesian Causal Forest (BCF) model, particularly the inclusion of the estimated propensity score $\hat{\pi}(x_i)$ intended to mitigate regularization-induced confounding (RIC). Through a partial ablation study utilizing a total of nine synthetic, we demonstrate that excluding $\hat{\pi}(x_i)$ does not diminish the model's performance in estimating average and conditional average treatment effects or in uncertainty quantification. Moreover, omitting $\hat{\pi}(x_i)$ reduces computational time by approximately 21%. These findings could suggest that the BCF model's inherent flexibility suffices in adjusting for confounding without explicitly incorporating the propensity score. The study advocates for the routine use of ablation studies in treatment effect estimation to ensure model components are essential and to prevent unnecessary complexity.