Research
Publications
Uncovering the heterogeneous effects of particular policies or “treatments” is a key concern for researchers and policymakers. A common approach is to report average treatment effects across subgroups based on observable covariates. However, the choice of subgroups is crucial, as it poses the risk of p-hacking and requires balancing interpretability with granularity. This article proposes a non parametric approach to construct heterogeneous subgroups. The approach enables a flexible exploration of the trade-off between interpretability and the discovery of more granular heterogeneity by constructing a sequence of nested groupings, each with an optimality property. By integrating our approach with “honesty” and debiased machine learning, we provide valid inference about the average treatment effect of each group. We validate the proposed methodology through an empirical Monte Carlo study and apply it to revisit the impact of maternal smoking on birth weight. Consistent with prior research, we find stronger effects for children born to adult mothers. We further provide novel evidence that effects are more pronounced when prenatal care begins earlier.
Causal inference methods such as instrumental variables, regression discontinuity, and difference-in-differences are widely used to identify and estimate treatment effects. However, when outcomes are qualitative, their application poses fundamental challenges. This paper highlights these challenges and proposes an alternative framework that focuses on well-defined and interpretable estimands. We show that conventional identification assumptions suffice for identifying the new estimands and outline simple, intuitive estimation strategies that remain fully compatible with conventional econometric methods. We provide an accompanying open-source R package, causalQual, which is publicly available on CRAN.
Empirical studies in various social sciences often involve categorical outcomes with inherent ordering, such as self-evaluations of subjective well-being and self-assessments in health domains. While ordered choice models, such as the ordered logit and ordered probit, are popular tools for analyzing these outcomes, they may impose restrictive parametric and distributional assumptions. This article introduces a novel estimator, the ordered correlation forest, that can naturally handle non linearities in the data and does not assume a specific error term distribution. The proposed estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class. Under an “honesty” condition, predictions are consistent and asymptotically normal. The weights induced by each forest are used to obtain standard errors for the predicted probabilities and the covariates’ marginal effects. Evidence from synthetic data shows that the proposed estimator features a superior prediction performance than alternative forest-based estimators and demonstrates its ability to construct valid confidence intervals for the covariates’ marginal effects. Comparisons using various real-world data sets further highlight the advantages of forest-based estimators over parametric models in larger samples while showing that the ordered correlation forest remains competitive in smaller samples.
Working papers
Many individuals hesitate to disclose their sexual orientation, anticipating that disclosure may alter how others respond to them. At the same time, concealing one's identity can entail substantial personal and social costs. Understanding how others react to sexual orientation disclosure is therefore central to evaluating the broader consequences of coming out. This paper uses an innovative data set from a popular online video game together with a natural experiment to causally identify behavioral responses to sexual minority disclosure. We exploit exogenous variation in the identity of a playable character to identify the effects of coming out on players' revealed preferences for that character across diverse regions globally. Our findings reveal a substantial and persistent negative impact of coming out.