Ablation Studies for Novel Treatment Effect Estimation Models
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Abstract (arXiv)
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 hatπ(xi) intended to mitigate regularization-induced confounding (RIC). Through a partial ablation study utilizing a total of nine synthetic, we demonstrate that excluding hatπ(xi) does not diminish the model's performance in estimating average and conditional average treatment effects or in uncertainty quantification. Moreover, omitting hatπ(xi) 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 21 Oct 2024.
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4 more
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