7 papers
Identification of Causal Effects with a Bunching Design
Carolina Caetano, Gregorio Caetano, Leonard Goff +1
We show that causal effects can be identified when there is bunching in the distribution of a continuous treatment variable, without imposing any parametric assumptions. This yield…
Testing the Solvability of Systems of Linear Inequalities
Leonard Goff, Eric Mbakop
This paper studies the problem of testing whether a system of linear equality and inequality constraints admits a solution when the coefficients of that system may have to be estim…
Testing Identifying Assumptions in Parametric Separable Models: A Conditional Moment Inequality Approach
Leonard Goff, Désiré Kédagni, Huan Wu
In this paper, we propose a simple method for testing identifying assumptions in parametric separable models, namely treatment exogeneity, instrument validity, and/or homoskedastic…
When does IV identification not restrict outcomes?
Leonard Goff
Many identification results in instrumental variables (IV) models hold without requiring any restrictions on the distribution of potential outcomes, or how those outcomes are corre…
Identifying causal effects with subjective ordinal outcomes
Leonard Goff
Survey questions often ask respondents to select from ordered scales where the meanings of the categories are subjective, leaving each individual free to apply their own definition…
Partial Identification from Bunching at Kinks and Notches
Leonard Goff
This paper proposes a partial identification approach to using choices around a kink or notch as a means to overcome endogeneity in a general nonparametric choice model. I show tha…
A Vector Monotonicity Assumption for Multiple Instruments
Leonard Goff
When a researcher combines multiple instrumental variables for a single binary treatment, the monotonicity assumption of the local average treatment effects (LATE) framework can be…