Policy Analysis using Synthetic Controls in Continuous-Time
arXiv:2102.01577
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.
References in corpus (7)
- Continuous control with deep reinforcement learning
- Latent ODEs for Irregularly-Sampled Time Series
- Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
- Reliable Decision Support using Counterfactual Models
- Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
- Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
- Learning Neural Event Functions for Ordinary Differential Equations