Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
Daniel Csillag, Claudio Jose Struchiner, Guilherme Tegoni Goedert
Abstract
Many algorithms have been recently proposed for causal machine learning. Yet, there is little to no theory on their quality, especially considering finite samples. In this work, we propose a theory based on generalization bounds that provides such guarantees. By introducing a novel change-of-measure inequality, we are able to tightly bound the model loss in terms of the deviation of the treatment propensities over the population, which we show can be empirically limited. Our theory is fully rigorous and holds even in the face of hidden confounding and violations of positivity. We demonstrate our bounds on semi-synthetic and real data, showcasing their remarkable tightness and practical utility.
BibTeX
@inproceedings{
csillag2024generalization,
title={Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis},
author={Daniel Csillag and Claudio Jose Struchiner and Guilherme Tegoni Goedert},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=TejqrQBvll}
}