6 papers
The Effect of Omitted Variables on the Sign of Regression Coefficients
Matthew A. Masten, Alexandre Poirier
We show that, depending on how the impact of omitted variables is measured, it can be substantially easier for omitted variables to flip coefficient signs than to drive them to zer…
Assessing Sensitivity to IV Exclusion and Exogeneity without First Stage Monotonicity
Paul Diegert, Matthew A. Masten, Alexandre Poirier
Exclusion and exogeneity are core assumptions in instrumental variable (IV) analyses, but their empirical validity is often debated. This paper develops new sensitivity analyses fo…
An Axiomatic Approach to Comparing Sensitivity Parameters
Paul Diegert, Matthew A. Masten, Alexandre Poirier
Many methods are available for assessing the importance of omitted variables in linear regression. These methods typically make different, non-falsifiable assumptions. Hence the da…
Assessing Omitted Variable Bias when the Controls are Endogenous
Paul Diegert, Matthew A. Masten, Alexandre Poirier
Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods assess the impact of omitted variables on empirical con…
Quantifying the Internal Validity of Weighted Estimands
Alexandre Poirier, Tymon SÅoczyÅski
In this paper we study a class of weighted estimands, which we define as parameters that can be expressed as weighted averages of the underlying heterogeneous treatment effects. Th…
A General Approach to Relaxing Unconfoundedness
Matthew A. Masten, Alexandre Poirier, Muyang Ren
This paper defines a general class of relaxations of the unconfoundedness assumption. This class includes several previous approaches as special cases, including the marginal sensi…