We introduce new inference procedures for counterfactual and synthetic control methods for
policy evaluation. We recast the causal inference problem as a counterfactual prediction and …

This paper studies inference on treatment effects in aggregate panel data settings with a
single treated unit and many control units. We propose new methods for making inference …

We introduce a novel forecasting method employing global deep learning models for
estimating the causal effects of interventions across multiple units, incorporating …

This thesis addresses the challenges of causal effect estimation in complex real-world
scenarios by proposing a global forecasting model (GFM) with deep neural networks …

We explore an upper bound of the mean squared prediction error (MSPE) of an arbitrary
synthetic control (SC) method in predicting the counterfactual of a treated unit. This potential …