Causal Inference
Paper Session
Monday, Jan. 4, 2021 12:15 PM - 2:15 PM (EST)
- Chair: Peter Hull, University of Chicago
Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effect
Abstract
To estimate the dynamic effect of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effect heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the shortcomings of two-way fixed effects regression with leads and lags in comparison to our proposed estimator through an empirical application.On Policy Evaluation with Aggregate Time-Series Shocks
Abstract
We propose a general strategy for estimating the treatment effects when the only source of exogenous variation is a sequence of aggregate time-series shocks. We start by arguing that commonly used estimation procedures tend to ignore the crucial time-series aspects of the data. Next, we develop a graphical tool and a novel formal test and then illustrate the issues of the design using data from influential studies in development economics [Nunn and Qian, 2014] and macroeconomics [Nakamura and Steinsson, 2014]. Motivated by these studies, we construct a new estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of our estimator in the practically relevant case where both cross-sectional and time-series dimensions are of similar size. Finally, to provide causal interpretation for our estimator, we analyze a new causal model that allows for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.Measuring Racial Discrimination in Bail Decisions
Abstract
We develop new quasi-experimental tools to measure racial discrimination, due to either racial bias or statistical discrimination, in the context of bail decisions. We show that the omitted variables bias in observational release rate comparisons can be purged by using the quasi-random assignment of judges to estimate average race-specific misconduct risk. We find that nearly two-thirds of the average release rate disparity between white and Black defendants in New York City is due to racial discrimination. We then develop a hierarchical marginal treatment effects model to study the drivers of discrimination, finding evidence of both racial bias and statistical discrimination.The Degrees of Freedom of the Synthetic Control Method
Abstract
We provide closed-form expressions for the degrees of freedom of the synthetic control method, with and without covariates. On the one hand, these results are conceptually informative: in spite of the extensive implicit model selection typically carried out by the method, the degrees of freedom expression suggests that it is in fact not prone to overfitting. On the other hand, these results are methodologically useful: while implementing cross-validation may be challenging with short series, an unbiased estimate of the degrees of freedom allows to circumvent cross-validation altogether and instead rely on information criteria to estimate out-of-sample performance.JEL Classifications
- C1 - Econometric and Statistical Methods and Methodology: General