It is becoming increasingly popular in applications of standard synthetic control methods to
include the entire pretreatment path of the outcome variable as economic predictors. We …

We analyze the properties of the Synthetic Control (SC) and related estimators when the pre‐
treatment fit is imperfect. In this framework, we show that these estimators are generally …

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 …

We present a new perspective on the Synthetic Control (SC) method as a weighted least
squares regression estimator with time fixed effects and unit weights. This perspective …

We extend the inference procedure for the synthetic control method in two ways. First, we
propose parametric weights for the p-value that includes the equal weights benchmark of …

We derive an inference method that works in differences-in-differences settings with few
treated and many control groups in the presence of heteroskedasticity. As a leading …

End-to-end deep learning models are increasingly applied to safety-critical human activity
recognition (HAR) applications, eg, healthcare monitoring and smart home control, to reduce …

Many countries have phased out nuclear power in response to concerns about nuclear
waste and the risk of nuclear accidents. This paper examines the shutdown of more than half …

In the aftermath of the taper tantrum, the Central Bank of Brazil announced a major program
of sterilized foreign exchange intervention. We use a synthetic control approach to estimate …

This paper develops the inferential theory for latent factor models estimated from large
dimensional panel data with missing observations. We propose an easy-to-use all-purpose …