Reverse Regression and Salary Discrimination

By A. Goldberger. 1984

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Citations in the corpus

Causal Generative Neural Networks (2017, background): “s X 2”. However, since X 2 provides a stronger signal-to-noise ratio for the prediction of Y, the least-squares solution to this problem is Y^ = 0:25X 1 +0:5X 2, a typical case of inverse regression (Goldberger, 1984). Such least-squares prediction would explain some changes in Y as a function of ch”
Should the DEA's STRIDE Data Be Used for Economic Analyses of Markets for Illegal Drugs? (2001, methodology): “Rather, as has been explained by Goldberger (1984), they are straightforward properties of “forward” and “reverse” regressions.”
Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, unknown): “5 Z The above regression model, a typical case of inverse regression after Goldberger (1984), would wrongly explain some changes in Y as a function of changes in Z , although Z does not cause Y .”
Linear Models: A Useful “Microscope” for Causal Analysis (2013, background): “Is it possible that men would earn a higher salary than equally qualified women, and simultaneously, men are more qualified than women doing equally paying job? This counter-intuitive condition can indeed exist, and has given rise to a controversy called “Reverse Regression;”' some sociologists argu”
Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models (2016, methodology): “The reverse regression literature in economics uses these estimates as a preprocessing step (Goldberger, 1984).”
Interpretation and identification of causal mediation. (2014, unknown): “For example, in coping with the age-old problem of gender discrimination (Bickel, Hammel, & O’Connell, 1975; Goldberger, 1984), a policymaker may be interested in assessing the extent to which gender disparity in hiring can be reduced by making hiring decisions gender-blind, compared with eliminatin”
Causal and Counterfactual Inference (2018, background): “For example, in coping with the age-old problem of gender discrimination (Bickel et al., 1975; Goldberger, 1984) a policy maker may be interested in assessing the extent to which gender disparity inf hiring can be reduced by making hiring decisions gender-blind, compared with eliminating gender ineq”
The Sports Business as a Labor Market Laboratory (2000, background): “In fact, these larger effects suggest that black players had better unmeasured productivity characteristics than whites, at least under the statistical assumptions outlined by Goldberger (1984). ferent points of the salary distribution, conditional on productivity.”