Papers & Manuscripts

1. Methodological Research for Causal Inference and Machine Learning

1.1 Causal Machine Learning for Heterogeneous Treatment Effects


1. Bargagli Stoffi, F. J., De Witte, K., Gnecco. G. (2022). Heterogeneous Causal Effects with Imperfect Compliance: a Bayesian Machine Learning Approach. The Annals of Applied Statistics, 16 (3), 1986-2009. American Causal Inference Conference 2019 Tom Ten Have Award Runner Up.

[pdf] [arXiv] [code] [cite

Coverage: [R-bloggers post] [YoungStats post]​

2. Bargagli Stoffi, F.J., Gnecco, G. (2020). Causal Tree with Instrumental Variable: An Extension of the Causal Tree Framework to Irregular Assignment Mechanisms. International Journal of Data Science and Analytics, 9, 315–337.

[paper] [cite]​

3. Bargagli Stoffi, F.J., Gnecco, G. (2018). Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms. In IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE DSAA Travel Award Paper.

[paper] [preprint] [cite]


4. Bargagli Stoffi, F.J.*, Tortù, C.*, Forastiere, L. Network Causal Tree: Heterogeneous Treatment and Spillover Effects under Clustered Network Interference. arXiv preprint arXiv:2008.00707.
Atlantic Causal Inference Conference 2022 Tom Ten Have Award Honorable Mention.

[pdf[preprint] [cite

5. Bargagli Stoffi, F.J., Lee, K., Cadei, R., Dominici, F. Causal Rule Ensemble: An ensemble Learning Approach for Interpretable Discovery of Heterogeneous Subgroups. arXiv preprint arXiv:2009.09036. 

[pdf[preprint]​​​​ [cite]


1.2 Interpretability and Simplicity in Causality and Machine Learning

6. Bargagli Stoffi, F.J., Cevolani, G., & Gnecco, G. (2022). Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory. Minds and Machines, 32(1), 13-42.

[paper] [cite]

7. Bargagli Stoffi, F.J., Cevolani, G., & Gnecco, G. (2021). Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? In International Conference on Machine Learning, Optimization, and Data Science (LOD) (pp. 55-59). Springer, Cham.

[paper] [cite]

2. Applications in Public Health, Economics and Public Policy

2.1 Public Health


8. Li, L., Dominici F., Blomberg, A., Bargagli Stoffi, F.J., Schwartz, J., Coull, B., Spengler, J., Wei, Y., Lawrence, J., Koutrakis, P. (2022). Exposure to Unconventional Oil and Gas Development and All-cause Mortality in Medicare Beneficiaries. Nature Energy, 7, 177–185.

[pdf] [paper] [code] [cite

Coverage: [The Guardian] [Science Daily] [The Hill] [Phys

9. Wasfy, J. H., Bargagli Stoffi, F.J. (2021). Assessing the Value of Echocardiography in the Absence of Randomized Trials: How Analytic Techniques from Causal Inference Can Fill the Gap. Journal of the American Society of Echocardiography, 34(6), 582-584.

[paper] [cite]

10. Bargagli Stoffi, F.J., Qin, M., Fairbank, N., Bennett, L., Butler, K., Braun, D., Dominici, F. Increasing the Distance Between Schools and the Nearest Gun Dealers Reduces the Risk of School Gun Incident: a National Analysis in the United States.

[preprint available upon request]

11. Biliotti, C.*, Bargagli Stoffi, F.J.*, Fraccaroli, N., Puliga,M., Riccaboni, M. Breaking Down the Lockdown: A Causal Analysis of Uncertainty and Public Reaction to the First Western COVID-19 Stay-at- Home Mandate. 


2.2 Public Policy

12. Incerti, F., Bargagli Stoffi, F.J., Riccaboni, M. (2022) A Two-Country Study of Default Risk Prediction using Bayesian Machine-Learning. In International Conference on Machine Learning, Optimization, and Data Science (LOD), forthcoming


13. Bargagli Stoffi, F.J., Riccaboni, M., Rungi, A. Machine Learning for Zombie Hunting. Predicting Distress from Firms’ Accounts and Missing Values.

Credit Scoring and Credit Rating Conference Best Paper Award.

[pdf] [preprint] [cite]

14. Bargagli Stoffi, F.J., De Beckker, K., Maldonado, J. E., & De Witte, K. Assessing Sensitivity of Machine learning Predictions. A Novel Toolbox with an Application to Financial Literacy. arXiv
preprint arXiv:2102.04382. 

[pdf] [preprint] [code] [cite]

15. Byrne, S., Reynolds, A.P.F., Biliotti, C., Bargagli Stoffi, F.J., Polonio, L., Riccaboni, R. Predicting Choice Behaviour in Economic Games using Gaze Data Encoded as Scanpath Images.

16. Ladant, F.X., Sestito, P., Bargagli Stoffi, F.J.. (2022), Rather First in a Village than Second at Rome: The Effect of Primary School Ordinal Rank on Subsequent Academic Achievements.


Book Chapters & Review Papers

17. Dominici, F., Bargagli Stoffi, F.J., Mealli, F. (2021). From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science using a Potential Outcome Framework.
Harvard Data Science Review, 3(3).

[paper] [preprint]

18. Bargagli Stoffi, F.J., Niederreiter, J., & Riccaboni, M. (2021). Supervised Learning for the Prediction of Firm Dynamics. In Data Science for Economics and Finance (pp. 19-41) Consoli, S., Reforgiato Recupero, D., & Saisana, M. (Eds.). Springer, Cham.
Most downloaded book from July 2021 to July 2022 among all Springer books in STM (Science and Technology) and in HSS (Human and Social Sciences) of the same period.

[paper] [preprint] [code] [cite]

19. Smet, M., D’Inverno, G., Tierens, H., De Witte, K., & Bargagli Stoffi, F.J., (2021). The Effectiveness of the Equal Educational Opportunity Policy (In Dutch, original title: De Effectiviteit van Het GOK-Beleid). In Differences in Education – Striving for Excellence and Equal Educational Opportunities. (pp. 255-276) G. Devos, M. Tuytens (Eds.). Politeia.



20. Bargagli Stoffi, F.J. (2020). Essays in Applied Machine Learning. Ph.D. Thesis, KU Leuven and IMT School for Advanced Studies


In Preparation

1. Bargagli Stoffi, F.J.*, Katz-Christy, N.*, Nethery, R., Dominici, F. Data-driven Heterogeneity Detection Among Subgroup-Specific Exposure-Response Functions.

2. McFarlin, E.*, Bargagli Stoffi, F.J.*, Castro, E., Amini H., Dominici, F. Air Quality Disparities Mapper: An Open-Source Web Application for Environmental Justice.

[preprint coming soon]

3. Zorzetto, D., Bargagli Stoffi, F.J., Canale, A., Dominici, F. Counfonder-Dependent Mixture Model: Bayesian Nonparametrics for Heterogeneity in Causal Effects.

4. Chen, K.L., Bargagli Stoffi, F.J., Kim, R., Nethery, R. Estimating Heterogeneous Causal Effects under Bipartite Network Interference.

5. Cadei, R., Delaney, S., Bargagli Stoffi, F.J. Vulnerability in Exposure to Air Pollution.


Indicates co-first authorship.