In this paper we study estimation of and inference for average treatment effects in a setting
with panel data. We focus on the staggered adoption setting where units, eg, individuals …

Based on the panel data of prefecture-level and above cities in China, this paper uses a
combination of Difference-in-Difference and trajectory balancing methods to investigate the …

The synthetic control method (SCM) is a popular approach for estimating the impact of a
treatment on a single unit in panel data settings. The “synthetic control” is a weighted …

This paper introduces a simple framework of counterfactual estimation for causal inference
with time‐series cross‐sectional data, in which we estimate the average treatment effect on …

This article studies the consequences of digital surveillance in dictatorships. I first develop
an informational theory of repression and co‐optation. I argue that digital surveillance …

Evaluating the impacts of population-level interventions (eg, changes to state legislation)
can be challenging as conducting randomized experiments is often impractical and …

This chapter surveys new development in causal inference using time-series cross-sectional
(TSCS) data. I start by clarifying two identification regimes for TSCS analysis: one under the …

Staggered adoption of policies by different units at different times creates promising
opportunities for observational causal inference. Estimation remains challenging, however …

This paper proposes a Bayesian alternative to the synthetic control method for comparative
case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive …

The relationship between anxiety and investor behavior is well known enough to warrant its
own aphorism: a “flight to safety.” We posit that anxiety alters the intensity of voters' …