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Poster
Policy Analysis using Synthetic Controls in Continuous-Time
Alexis Bellot · Mihaela van der Schaar

Wed Jul 21 09:00 PM -- 11:00 PM (PDT) @ None #None

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.

Author Information

Alexis Bellot (University of Cambridge)
Mihaela van der Schaar (University of Cambridge and UCLA)

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