Work in Progress
Econometrics
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Average Treatment Effects Conditional on Unobservables: A Welfare Maximization Approach.
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Recovering Partially-Identified Causal Parameters in Difference-in-Differences using Optimal Transport. [Show Abstract]
Some families of causal parameters are a function of the joint distribution of potential outcomes and thus can only be partially identified. Framing identification as an optimal transport problem, I construct sharp bounds that are valid even under arbitrary model misspecification. Both continuous and discrete covariates can be used to tighten the identified set, which does not inherit any bias from incorrect model specification. This approach requires a so-called distributional difference-in-differences assumption, which holds by construction in RCTs and can be defended in many nonexperimental settings with panel data. I finally study applications with real and simulated data.
Applied Economics
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)