Research

Work in Progress

Econometrics

• Distributionally-Robust Difference in Differences.

• Causal Inference with Tensor-Valued Short Panels [Show Abstract]

Many complex economic phenomena can be well-approximated by models that decompose outcomes into a low-dimensional signal plus noise. This structural parsimony underpins research designs such as synthetic controls and matrix completion, which explicitly exploit it to construct valid counterfactuals. As empirical settings become more data-rich, time series observed in tensor form (that is, as higher-order arrays) have gained popularity in economics, finance, medical research and other fields. Despite their higher dimensionality, tensors still admit low-dimensional representations. Existing causal and inferential methods for policy evaluation with tensor data are scarce and typically rely on large-N, large-T asymptotics. Building on recent work by Brown and Butts (2025) and Lei and Ross (2024), I plan to extend bridge functions to multi-dimensional data structures and characterize the conditions under which policy effects remain identifiable when only a limited number of time periods are observed. Methodologically, this early-stage proposal draws on the literature on imputation estimators for two-way fixed effects models, with two key modifications: it accommodates nonadditive unobserved heterogeneity and explicitly exploits the tensor structure of the data.

Applied Economics

• Evaluating the Effect of Training Programs for the Unemployed: An Examiner Design Approach (with Luc Behaghel, Marc Gurgand and Yagan Hazard). [Show Abstract]

We exploit the random allocation of caseworkers to job seekers in France---and the heterogeneity in caseworkers' propensity to place individuals in training programs---in order to build an instrument for entering a training program while unemployed. To alleviate threats to the exclusion restriction assumption, we are currently developing an identification approach combining (i) the intuition behind of so-called ''zero-first-stage'' falsification test, (ii) an identification-at-infinity argument and (iii) a single-index assumption imposed on caseworkers' direct impact on individuals' job finding rate (violating the exclusion restriction of the instrument). Our framework lends itself nicely to the use of machine-learning predictions in a first step to identify the zero-first-stage subgroups that are essential for our identification-at-infinity approach.

Publications

Applied Economics

• Cost-effectiveness analysis of the daily HIV pre-exposure prophylaxis in men who have sex with men in Barcelona (PLOS ONE, 2023)