Title: Classification Algorithms & Social Outcomes Abstract: Classification algorithms are increasingly important in areas such as obtaining credit, employment, health care, housing, law enforcement, and national security. These classification decisions

Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects Randomization inference (RI) is typically interpreted as testing Fisher's "sharp" null hypothesis that all unit-level

Covariates in the eforensics Model Walter R. Mebane, Jr. Election forensics is the use of statistical methods to determine whether the results of an election accurately reflect the intentions of

Factorial Difference-in-DifferencesDownload paper In many social science applications, researchers use the difference-in-differences (DID) estimator to establish causal relationships, exploiting cross-sectional variation in a baseline factor and temporal variation in exposure

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Title: Inference in Cluster randomized Trials with Matched Pairs Abstract: This paper considers the problem of inference in cluster randomized experiments when cluster sizes are non-ignorable. Here, by a cluster