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Found 8 papers, 0 papers with code
Testing Sign Congruence
We consider testing the null hypothesis that two parameters $({\mu}_1, {\mu}_2)$ have the same sign, assuming that (asymptotically) normal estimators are available.
Inference for an Algorithmic Fairness-Accuracy Frontier
In this paper, we provide a consistent estimator for a theoretical fairness-accuracy frontier put forward by Liang, Lu and Mu (2023) and propose inference methods to test hypotheses that have received much attention in the fairness literature, such as (i) whether fully excluding a covariate from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to an existing algorithm.
Information Based Inference in Models with Set-Valued Predictions and Misspecification
This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified.
Risk Preference Types, Limited Consideration, and Welfare
We provide sufficient conditions for semi-nonparametric point identification of a mixture model of decision making under risk, when agents make choices in multiple lines of insurance coverage (contexts) by purchasing a bundle.
Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection.
Constraint Qualifications in Partial Identification
The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications.
Heterogeneous Choice Sets and Preferences
We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance.
Discrete Choice under Risk with Limited Consideration
This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives.
