The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption that the treatment group, absent treatment, would change "similarly" to the control group over time. To increase the plausibility of this...
Social scientists have developed dozens of measures for assessing partisan bias in redistricting. But these measures cannot be easily adapted to other groups, including those defined by race, class, or geography. Nor are they applicable to single- or no...
Conventional causal estimands, such as the average treatment effect (ATE), reflect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often...
