The roots of inequality: estimating inequality of opportunity from regression trees and forests*
提出用回归树和随机森林等机器学习方法估计机会不平等，相比传统方法能减少模型选择的随意性，并平衡估计中的向上和向下偏差。对31个欧洲国家的实证分析显示，任意模型选择会导致显著偏差，影响估计值和国家排名。
Abstract We propose the use of machine learning methods to estimate inequality of opportunity and to illustrate that regression trees and forests represent a substantial improvement over existing approaches: they reduce the risk of ad hoc model selection and trade off upward and downward bias in inequality of opportunity estimates. The advantages of regression trees and forests are illustrated by an empirical application for a cross‐section of 31 European countries. We show that arbitrary model selection might lead to significant biases in inequality of opportunity estimates relative to our preferred method. These biases are reflected in both point estimates and country rankings.