6 papers
Finite Population Identification and Design-Based Sensitivity Analysis
Brendan Kline, Matthew A. Masten
We develop a new approach for quantifying uncertainty in finite populations, by using design distributions to calibrate sensitivity parameters in finite population identified sets.…
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…
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…