Working Papers
- Locally Robust Policy Learning: Inequality, Inequality of Opportunity and Intergenerational Mobility
- Abstract: Policy makers need to decide whether to treat or not to treat different individuals. The optimal choice depends on the welfare function that the policy maker has in mind. I study a general setting for policy learning with general semiparametric Social Welfare Functions (SWFs), possibly defined by semiparametric U-statistics, which accomodate a wide range of distributional preferences. I use locally robust/orthogonal scores to provide strong statistical guarantees for the estimated policy rules even in observational settings where the propensity score is unkown. This work expands previous results in Athey and Wager (2021). Three main applications of the general theory motivate the paper: (i) Inequality aware SWFs, (ii) Inequality of Opportunity aware SWFs and (iii) Intergenerational Mobility SWFs. I use the Panel Study of Income Dynamics (PSID) to asses the effect of attending preschool on adult earnings and estimate optimal policy rules based on parental years of education and parental income. Cite.
- Debiased Machine Learning U-statistics: with
Juan Carlos Escanciano, R&R at Review of Economic Studies, previously distributed as Machine Learning Inference on Inequality of Opportunity.
- Abstract: We propose a method to debias estimators based on U-statistics with Machine Learning (ML) first-steps. Standard plug-in estimators often suffer from regularization and model-selection biases, producing invalid inferences. We show that Debiased Machine Learning (DML) estimators can be constructed within a U-statistics framework to correct these biases while preserving desirable statistical properties. The approach delivers simple, robust estimators with provable asymptotic normality and good finite-sample performance. We apply our method to three problems: inference on Inequality of Opportunity (IOp) using the Gini coefficient of ML-predicted incomes given circumstances, inference on predictive accuracy via the Area Under the Curve (AUC), and inference on linear models with ML-based sample-selection corrections. Using European survey data, we present the first debiased estimates of income IOp. In our empirical application, commonly employed ML-based plug-in estimators systematically underestimate IOp, while our debiased estimators are robust across ML methods. Cite.
Books
- Causal Inference and Machine Learning: A Locally Robust Approach: with
Juan Carlos Escanciano, in progress (Taylor & Francis Group)