2021-07-01-bellot21a.md

title	abstract	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
Policy Analysis using Synthetic Controls in Continuous-Time	Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. Despite its popularity, the current description only considers time series aligned across units and synthetic controls expressed as linear combinations of observed control units. We propose a continuous-time alternative that models the latent counterfactual path explicitly using the formalism of controlled differential equations. This model is directly applicable to the general setting of irregularly-aligned multivariate time series and may be optimized in rich function spaces – thereby improving on some limitations of existing approaches.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bellot21a	0	Policy Analysis using Synthetic Controls in Continuous-Time	759	768	759-768	759	false	Bellot, Alexis and van der Schaar, Mihaela	given family Alexis Bellot given prefix family Mihaela van der Schaar	2021-07-01		Proceedings of the 38th International Conference on Machine Learning	139	inproceedings	date-parts 2021 7 1	http://proceedings.mlr.press/v139/bellot21a/bellot21a.pdf	label link Supplementary ZIP http://proceedings.mlr.press/v139/bellot21a/bellot21a-supp.zip