# Ablation Studies for Novel Treatment Effect Estimation Models

arXiv: https://arxiv.org/abs/2410.15560 · HTML: https://mlsanity.com/paper/2410.15560/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting](https://mlsanity.com/paper/2103.07719/) (2021)
- [The Reversible Residual Network: Backpropagation Without Storing Activations](https://mlsanity.com/paper/1707.04585/) (2017)
- [Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho](https://mlsanity.com/paper/2108.02836/) (2021)
- [Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx](https://mlsanity.com/paper/2104.05522/) (2021)
- [Ablation Studies in Artificial Neural Networks](https://mlsanity.com/paper/1901.08644/) (2019)
- [Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures](https://mlsanity.com/paper/1608.06037/) (2016)
- [Regularization and confounding in linear regression for treatment effect estimation](https://mlsanity.com/paper/1602.02176/) (2016)
- [Matching on Generalized Propensity Scores with Continuous Exposures](https://mlsanity.com/paper/1812.06575/) (2018)
- [Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration](https://mlsanity.com/paper/2409.04874/) (2024)
- [Aggregate Bayesian Causal Forests: The ABCs of Flexible Causal Inference for Hierarchically Structured Data](https://mlsanity.com/paper/2407.07067/) (2024)
- [Beyond Arbitrary Replications: A Principled Approach to Simulation Design in Causal Inference](https://mlsanity.com/paper/2409.05161/) (2024)
- [Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments](https://mlsanity.com/paper/2409.06593/) (2024)

## Built on by (most cited first, 1 of 1)

- [Forests for Differences: Robust Causal Inference Beyond Parametric DiD](https://mlsanity.com/paper/2505.09706/) (2025)

## 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.