We consider the problem of constructing bounds on the average treatment effect (ATE) when
unmeasured confounders exist but have bounded influence. Specifically, we assume that …

Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from
observational data. However, its correctness relies on the untestable (and frequently …

We implement a lab-in-the-field experiment with 334 Turkish loan officers to document
gender discrimination in small business lending and unpack mechanisms. Officers review …

In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a
weighted average of conditional local average treatment effects (LATEs). I focus on a …

This article considers the evaluation of discretely distributed treatments when outcomes are
only observed for a subpopulation due to sample selection or outcome attrition. For …

Many causal estimands are only partially identifiable since they depend on the
unobservable joint distribution between potential outcomes. Stratification on pretreatment …

Truncated conditional expectation functions are objects of interest in a wide range of
economic applications, including income inequality measurement, financial risk …

Sample selection problems arise when treatment affects both the outcome and the
researcher's ability to observe it. This paper generalizes Lee (2009) bounds for the average …

We propose a method for estimation and inference for bounds for heterogeneous causal
effect parameters in general sample selection models where the treatment can affect …

This paper investigates the causal effect of job training on wage rates in the presence of firm
heterogeneity. When training affects worker sorting to firms, sample selection is no longer …