This paper exposits and relates two distinct approaches to bounding the average treatment effect. One approach, based on instrumental variables, is due to Manski (1990, 1994), who derives tight bounds on the average treatment effect under a mean independence form of the instrumental variables...

This paper introduces an instrumental variables estimator for the effect of a binary treatment on the quantiles of potential outcomes. The quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression as a special case when treatment status is...

Given the ubiquitous presence of endogenous regressors and the challenges in finding good instruments to overcome the endogeneity problem, a forefront of recent research is the development and application of endogeneity correction methods without requiring instruments. In this article, we...

Structural econometric methods are often criticized for being sensitive to functional form assumptions. We study parametric estimators of the local average treatment effect (LATE) derived from a widely used class of latent threshold crossing models and show they yield LATE estimates...

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

The average effect of intervention or treatment is a parameter of interest in both epidemiology and econometrics. A key difference between applications in the two fields is that epidemiologic research is more likely to involve qualitative outcomes and nonlinear models. An example is the recent...

Causal inference methods are widely used in empirical research; however, there is a paucity of evidence on the properties of shared latent factor estimators in the presence of contaminated instrumental variable (IV) when a strong IV may not be available. We present a theoretical formulation to...

The effect of a treatment may depend on the intensity with which it is administered. We study identification of ordered treatment effects with a binary instrument, focusing on the effect of moving from the treatment's minimum to maximum intensity. With arbitrary heterogeneity across units,...

This chapter synthesizes and critically reviews the modern instrumental variables (IV) literature that allows for unobserved heterogeneity in treatment effects (UHTE). We start by discussing why UHTE is often an essential aspect of IV applications in economics and we explain the conceptual...

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...