Search Results for author:
Found 10 papers, 3 papers with code
Optimal Conditional Inference in Adaptive Experiments
We study batched bandit experiments and consider the problem of inference conditional on the realized stopping time, assignment probabilities, and target parameter, where all of these may be chosen adaptively using information up to the last batch of the experiment.
Empirical Bayes When Estimation Precision Predicts Parameters
Empirical Bayes methods usually maintain a prior independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates.
Logs with zeros? Some problems and solutions
When studying an outcome $Y$ that is weakly-positive but can equal zero (e. g. earnings), researchers frequently estimate an average treatment effect (ATE) for a "log-like" transformation that behaves like $\log(Y)$ for large $Y$ but is defined at zero (e. g. $\log(1+Y)$, $\mathrm{arcsinh}(Y)$).
JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices
Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change?
Synthetic Control As Online Linear Regression
This paper notes a simple connection between synthetic control and online learning.
Nonparametric Treatment Effect Identification in School Choice
In particular, many aggregations of aTEs put zero weight on aTEs driven by RD variation, and estimators of such aggregations put asymptotically vanishing weight on the RD-driven aTEs.
Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators
We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics.
Semiparametric Estimation of Long-Term Treatment Effects
Long-term outcomes of experimental evaluations are necessarily observed after long delays.
Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models
We offer straightforward theoretical results that justify incorporating machine learning in the standard linear instrumental variable setting.
