Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and...

The fast growing statistical literatures on matching methods in several disciplines offer the promise of causal inference without resort to the difficult-to-justify functional form assumptions inherent in commonly used parametric methods. However, these literatures also suffer from many diverse...

How does partisan regulation of political markets affect elections? We investigate how the partisan control of ballot format, which is expressly regulated - often to the apparent advantage of incumbents and major parties - in all U.S. states, affects voting. Through the analysis of a unique...

Since the 2000 U.S. Presidential election, social scientists have rediscovered a long tradition of research that investigates the effects of ballot format on voting. Using a new dataset collected by the New York Times, we investigate the causal effect of being listed on the first ballot page in...

Randomized natural experiments provide social scientists with rare opportunities to draw credible causal inferences in real-world settings. We capitalize on such a unique experiment to examine how the name order of candidates on ballots affects election outcomes. Since 1975, California has...

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of...

Government