Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

This article reviews recent significant progress made in developing estimation and inference
methods for nonlinear models in the presence of mismeasured data that may or may not …

Prediction-powered inference is a framework for performing valid statistical inference when
an experimental dataset is supplemented with predictions from a machine-learning system …

This paper studies the efficient estimation of a large class of multi-valued treatment effects as
implicitly defined by a collection of possibly over-identified non-smooth moment conditions …

While the literature on nonclassical measurement error traditionally relies on the availability
of an auxiliary data set containing correctly measured observations, we establish that the …

Recent years have seen widespread use of small-area maps based on census data
enriched by relationships estimated from household surveys that predict variables, such as …

In many experimental and observational studies, the outcome of interest is often difficult or
expensive to observe, reducing effective sample sizes for estimating average treatment …

We report results from a validation study of the Nielsen Homescan consumer panel data. We
use data from a large grocery retailer to match transactions that were recorded by the retailer …

We study semiparametric efficiency bounds and efficient estimation of parameters defined
through general moment restrictions with missing data. Identification relies on auxiliary data …

Economists who use survey or administrative data for inferences regarding a population
may want to combine information obtained from two or more samples drawn from the …