I am a PhD Candidate in the Economics Department at Northwestern University, and a job market candidate for the 2025/26 season. I am an econometrician also working on empirical development economics, with a focus on causal inference and machine learning methods. My research develops new econometric methods motivated by empirical challenges in development economics, and I use theoretical insights and contextual knowledge to identify and answer questions in my applied work.

Job Market Paper

A Central Limit Theorem for Split-Sample Estimators

As artificial intelligence grows in popularity, an increasing number of researchers, policymakers and industry practitioners are tasked with using the same dataset to both train a new model and evaluate some of its properties, such as accuracy and fairness. Conducting each task with separate random splits of the data facilitates inference, but comes at the cost of using fewer data at each step. Averaging over multiple splits, such as with cross-fitting, uses more data for each task and leads to better reproducibility properties, but inference is challenging due to the statistical dependence among splits. I address this challenge by proving a new Central Limit Theorem for a large class of split-sample estimators under arguably mild and general conditions. Importantly, I make no restriction on the model complexity, convergence rate or rate of algorithmic stability. I show that the dependence across splits is asymptotically negligible in many cases, document cases when it matters, and provide confidence intervals that explicitly account for the dependence structure. Moreover, I provide a measure of reproducibility for p-values obtained from split-sample estimators, and show its asymptotic validity. I apply my results to predicting poverty in Ghana and show that my confidence intervals are able to detect a subgroup at elevated risk of falling below the extreme poverty line, while previous methods do not. Finally, I revisit the problem of learning heterogeneous treatment effects in randomized experiments and develop an ensemble method that combines predictions across multiple predictors. In simulations and an empirical application to charitable giving, the ensemble method achieves greater power than previous approaches in detecting treatment effect heterogeneity.

Working Papers

Predicting the Distribution of Treatment Effects via Covariate-Adjustment, with an Application to Microcredit [arxiv] [Presentation MEG 2024]
Best Student Paper Award at the 32nd Midwest Econometrics Group Annual Conference (2024)
Important questions for impact evaluation require knowledge not only of average effects, but of the distribution of treatment effects. The inability to observe individual counterfactuals makes answering these empirical questions challenging. I propose an inference approach for points of the distribution of treatment effects by incorporating predicted counterfactuals through covariate adjustment. I provide finite-sample valid inference using sample-splitting, and asymptotically valid inference using cross-fitting, under arguably weak conditions. Revisiting five randomized controlled trials on microcredit that reported null average effects, I find important distributional impacts, with some individuals helped and others harmed by the increased credit access.

Publications

Probabilistic Nearest Neighbors Classification [pdf] [R Package]
(with Paulo C. Marques F. and Hedibert F. Lopes) Entropy, 2024, 26(1), 39.
Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.
The Illusion of the Illusion of Sparsity: An exercise in prior sensitivity [pdf] [code]
(with Hedibert F. Lopes) Brazilian Journal of Probability and Statistics, 2021, Vol. 35, No. 4, 699-720.
The emergence of Big Data raises the question of how to model economic relations when there is a large number of possible explanatory variables. We revisit the issue by comparing the possibility of using dense or sparse models in a Bayesian approach, allowing for variable selection and shrinkage. More specifically, we discuss the results reached by Giannone, Lenza and Primiceri (2020) through a “Spike-and-Slab” prior, which suggest an “illusion of sparsity” in Economics datasets, as no clear patterns of sparsity could be detected. We make a further revision of the posterior distributions of the model, and propose three experiments to evaluate the robustness of the adopted prior distribution. We find that the pattern of sparsity is sensitive to the prior distribution of the regression coefficients, and present evidence that the model indirectly induces variable selection and shrinkage, which suggests that the “illusion of sparsity” could be, itself, an illusion. Code is available on Github.

Work in Progress

Algorithmic Bias in Microcredit: Consequences of Data-Driven Lending Practices
(with Susan Athey, Dean Karlan, Adam Osman, and Jonathan Zinman)
Is Participant Feedback Predictive of Impact?
(with Gharad Bryan, Dean Karlan, Isabel Oñate, and Christopher Udry)
What Can We Learn from Harmonizing and Analyzing RCTs of Grant and Training Programs to Promote Entrepreneurship?
(with Florian de Bundel, Dean Karlan, William Parienté, and Christopher Udry)

Bruno Fava

Job Market Paper

Working Papers

Publications

Work in Progress