41 citations · 54 across the 10 of their papers we have counts for
13 papers
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…
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.…
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…
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 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…
Choosing Exogeneity Assumptions in Potential Outcome Models
Matthew A. Masten, Alexandre Poirier
There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the…
Assessing Sensitivity to Unconfoundedness: Estimation and Inference
Matthew A. Masten, Alexandre Poirier, Linqi Zhang
This paper provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption (also known as selection on observables or…
Salvaging Falsified Instrumental Variable Models
Matthew A. Masten, Alexandre Poirier
What should researchers do when their baseline model is refuted? We provide four constructive answers. First, researchers can measure the extent of falsification. To do this, we co…
Interpreting Quantile Independence
Matthew A. Masten, Alexandre Poirier
How should one assess the credibility of assumptions weaker than statistical independence, like quantile independence? In the context of identifying causal effects of a treatment v…
Identification of Treatment Effects under Conditional Partial Independence
Matthew A. Masten, Alexandre Poirier
Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect para…
Inference on Breakdown Frontiers
Matthew A. Masten, Alexandre Poirier
Given a set of baseline assumptions, a breakdown frontier is the boundary between the set of assumptions which lead to a specific conclusion and those which do not. In a potential…
Instrumental Variables Estimation of a Generalized Correlated Random Coefficients Model
Matthew A. Masten, Alexander Torgovitsky
We study identification and estimation of the average treatment effect in a correlated random coefficients model that allows for first stage heterogeneity and binary instruments. T…