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Found 7 papers, 0 papers with code
Debiasing Machine Learning Models by Using Weakly Supervised Learning
In our work, we then propose a bias mitigation strategy for continuous sensitive variables, based on the notion of endogeneity which comes from the field of econometrics.
Partly Linear Instrumental Variables Regressions without Smoothing on the Instruments
We consider a semiparametric partly linear model identified by instrumental variables.
Testing for homogeneous treatment effects in linear and nonparametric instrumental variable models
This paper develops two tests for the assumption of homogeneous treatment effects when the treatment is endogenous and an instrumental variable is available.
Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables
A {\it fair} solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space.
Revisiting identification concepts in Bayesian analysis
First, for unidentified models we demonstrate that there are situations where the introduction of a non-degenerate prior distribution can make a parameter that is nonidentified in frequentist theory identified in Bayesian theory.
Iterative Estimation of Nonparametric Regressions with Continuous Endogenous Variables and Discrete Instruments
We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term.
Is completeness necessary? Estimation in nonidentified linear models
We show that estimators based on spectral regularization converge to the best approximation of a structural parameter in a class of nonidentified linear ill-posed inverse models.
