2022-05-03-de-brouwer22a.md

title	abstract	software	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
Predicting the impact of treatments over time with uncertainty aware neural differential equations.	Predicting the impact of treatments from ob- servational data only still represents a major challenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefore in poor quality estimates. Developments in causal inference have lead to methods addressing this confounding by requiring a minimum level of overlap. However, overlap is difficult to assess and usually not satisfied in practice. In this work, we propose Counterfactual ODE (CF-ODE), a novel method to predict the impact of treatments continuously over time using Neural Ordinary Differential Equations equipped with uncertainty estimates. This allows to specifically assess which treatment outcomes can be reliably predicted. We demonstrate over several longitudinal datasets that CF-ODE provides more accurate predictions and more reliable uncertainty estimates than previously available methods.	https://github.com/microsoft/cf-ode	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	de-brouwer22a	0	Predicting the impact of treatments over time with uncertainty aware neural differential equations.	4705	4722	4705-4722	4705	false	De Brouwer, Edward and Gonzalez, Javier and Hyland, Stephanie	given family Edward De Brouwer given family Javier Gonzalez given family Stephanie Hyland	2022-05-03		Proceedings of The 25th International Conference on Artificial Intelligence and Statistics	151	inproceedings	date-parts 2022 5 3	https://proceedings.mlr.press/v151/de-brouwer22a/de-brouwer22a.pdf