2024-09-12-deshpande24a.md

title	abstract	openreview	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Calibrated and Conformal Propensity Scores for Causal Effect Estimation	Propensity scores are commonly used to balance observed covariates while estimating treatment effects. We argue that the probabilistic output of a learned propensity score model should be calibrated, i.e. a predictive treatment probability of 90% should correspond to 90% of individuals being assigned the treatment group. We propose simple recalibration techniques to ensure this property. We prove that calibration is a necessary condition for unbiased treatment effect estimation when using popular inverse propensity weighted and doubly robust estimators. We derive error bounds on causal effect estimates that directly relate to the quality of uncertainties provided by the probabilistic propensity score model and show that calibration strictly improves this error bound while also avoiding extreme propensity weights. We demonstrate improved causal effect estimation with calibrated propensity scores in several tasks including high-dimensional image covariates and genome-wide association studies (GWASs). Calibrated propensity scores improve the speed of GWAS analysis by more than two-fold by enabling the use of simpler models that are faster to train.	xgs9VpvVAC	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	deshpande24a	0	Calibrated and Conformal Propensity Scores for Causal Effect Estimation	1083	1111	1083-1111	1083	false	Deshpande, Shachi and Kuleshov, Volodymyr	given family Shachi Deshpande given family Volodymyr Kuleshov	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/deshpande24a/deshpande24a.pdf