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Francis J. DiTraglia

Associate Professor of Economics at the University of Oxford. Applied econometrician specializing in causal inference, instrumental variables, measurement error, spillovers, and Bayesian inference.

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# Francis J. DiTraglia

> Associate Professor of Economics at the University of Oxford. Applied econometrician specializing in causal inference, instrumental variables, measurement error, spillovers, and Bayesian inference.

Francis DiTraglia is a Tutor and Fellow in Economics at Lady Margaret Hall, Oxford, and a visiting researcher at the Federal Reserve Bank of Chicago. His research develops statistical methods for untangling cause-and-effect and applies them to questions in economics and public policy. Applied work spans childhood lead exposure and pawn lending. He is beginning to explore research questions related to AI safety and is a resident member of the London Initiative for Safe AI (LISA). He is co-founder of SQARE.org, maintainer of restatr.com, and blogs at econometrics.blog.

## Publications

- [Structured Payment in Pawnshop Borrowing: Mandates vs. Choice](https://arxiv.org/abs/2608.13775): Experimental study of structured repayment in Mexico City pawnshops (with McIntosh, Meza, Sadka, Seira). Review of Economic Studies, forthcoming.
- [Efficient Semiparametric Estimation of Marginal Treatment Effects with Genetic Instrumental Variables](https://ditraglia.com/pdf/MTE-genetic-IVs.pdf): Using a genetic instrument and efficient influence functions to study how the effect of heavy drinking on blood pressure varies with the propensity to drink (with Patel, Burgess). Annals of Applied Statistics, forthcoming.
- [Environmental Lead Risk in the 21st Century](https://rdcu.be/eIRSq): Mapping global environmental lead exposure risk using geochemical and epidemiological data (with Chen, Gazze, Das, Nriagu, Erel, Boyle, Taylor, Weiss). Communications Earth & Environment, 2025.
- [Selecting Invalid Instruments to Improve Mendelian Randomization with Two-Sample Summary Data](https://doi.org/10.1214/23-AOAS1856): Showing that deliberately including some invalid genetic instruments can reduce mean squared error in Mendelian randomization studies (with Patel, Zuber, Burgess). Annals of Applied Statistics, 2024. [Pre-print](https://arxiv.org/abs/2107.01513)
- [Hidden Hazards and Screening Policy: Predicting Undetected Lead Exposure in Illinois](https://doi.org/10.1016/j.jhealeco.2023.102783): Using machine learning to predict which children are most likely to have undetected lead poisoning (with Abbasi, Gazze, Pals). Journal of Health Economics, 2023. [Pre-print](https://ditraglia.com/pdf/lead-JHE.pdf)
- [Identifying Causal Effects in Experiments with Spillovers and Non-compliance](https://doi.org/10.1016/j.jeconom.2023.01.008): Methods for estimating treatment effects when subjects interact and don't always comply with treatment assignment (with García-Jimeno, O’Keeffe-O’Donovan, Sánchez-Becerra). Journal of Econometrics, 2023. [Pre-print](https://ditraglia.com/pdf/spillovers-paper.pdf)
- [A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models](https://doi.org/10.1080/07350015.2020.1753528): A Bayesian fram
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