Policy Analysis using Synthetic Controls in Continuous-Time

Alexis Bellot, Mihaela van der Schaar
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:759-768, 2021.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-bellot21a, title = {Policy Analysis using Synthetic Controls in Continuous-Time}, author = {Bellot, Alexis and van der Schaar, Mihaela}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {759--768}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/bellot21a/bellot21a.pdf}, url = {https://proceedings.mlr.press/v139/bellot21a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Policy Analysis using Synthetic Controls in Continuous-Time %A Alexis Bellot %A Mihaela van der Schaar %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-bellot21a %I PMLR %P 759--768 %U https://proceedings.mlr.press/v139/bellot21a.html %V 139 %X 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.
APA
Bellot, A. & van der Schaar, M.. (2021). Policy Analysis using Synthetic Controls in Continuous-Time. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:759-768 Available from https://proceedings.mlr.press/v139/bellot21a.html.

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