Dear colleagues,
We hereby invite you to join our next Economics
of Inequality Research Seminar at WU Vienna.
Joël Terschuur
(TU München)
will present the paper
Machine Learning Inference on Inequality of Opportunity.
Co-Author:
Juan Carlos Escanciano
Date and Time:
Monday, November 10; 3.00 p.m.
Location: WU Vienna,
TC – TC.54.15.
Abstract
Equality of opportunity has emerged as an important ideal of distributive justice. Empirically, Inequality of Opportunity (IOp) is measured in two steps: first, an outcome (e.g., income)
is predicted given individual circumstances; and second, an inequality index (e.g., Gini) of the predictions is computed. Machine Learning (ML) methods are tremendously useful in the first step. However, they can cause sizable biases in IOp since the bias-variance
trade-off allows the bias to creep in the second step. We propose a simple debiased IOp estimator robust to such ML biases and provide the first valid inferential theory for IOp. We demonstrate improved performance in simulations and report the first unbiased
measures of income IOp in Europe. Mother’s education and father’s occupation are the circumstances that explain the most. Plug-in estimators are very sensitive to the ML algorithm, while debiased IOp estimators are robust. These results are extended to a general
U-statistics setting.
Please register your participation
here.
Best regards,
Franziska Disslbacher
----
Franziska Disslbacher, PhD
Assistant Professor
Institute for Spatial & Social-Ecological Transformations
&
Research Institute Economics of Inequality
WU, Vienna University of Economics and Business
Welthandelsplatz 1, 1020 Vienna, Austria
LSE International Inequalities Institute – Research Fellow |
Stone Center on Socio-Economic Inequality CUNY |
SOEP / DIW – Associated Researcher
World Inequality Lab PSE– Research Fellow
+43 1 313 36 6669
franziska.disslbacher@wu.ac.at