The view that empirical strategies in economics should be transparent and credible now goes almost without saying. The local average treatment effects (LATE) framework for causal inference helped make this so. The LATE theorem tells us for whom particular instrumental variables (IV) and...

"In many countries, college-bound high school seniors must pass a test or series of tests. In Israel, this requirement is known as the "Bagrut", or matriculation certificate, obtained by passing a series of subject tests. In spite of the Bagrut's value, Israeli society is marked by vast...

Two-stage least squares estimates in heavily over-identified instrumental variables (IV) models can be misleadingly close to the corresponding ordinary least squares (OLS) estimates when many instruments are weak. Just-identified (just-ID) IV estimates using a single instrument are also biased,...

School assignment in Boston and New York City came to national attention in the 1970s as courts across the country tried to integrate schools. Today, district-wide choice allows Boston and New York students to enroll far from home, perhaps enhancing integration. Urban school transportation is...

Many personal and policy decisions turn on perceptions of school effectiveness, defined here as the causal effect of attendance at a particular school or set of schools on student test scores and other outcomes. Widely-disseminated school ratings frameworks compare average student achievement...

Pragmatic cancer screening trials mimic real-world scenarios in which patients and doctors are the ultimate arbiters of treatment. Intention-to-screen (ITS) analyses of such trials maintain randomization-based apples-to-apples comparisons, but differential adherence (the failure of subjects...

We develop a flexible semiparametric time series estimator that is then used to assess the causal effect of monetary policy interventions on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macro-dynamic setting, without the need...

Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...