My research interests span: 

  • causal inference

  • machine learning

  • heterogeneous causal effects

  • interpretability and simplicity in artificial intelligence

I am interested in applications in:

  • health sciences

  • air pollution

  • climate change

  • public policy

  • education

Published Papers

  • 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, in press. American Causal Inference Conference 2019 Tom Ten Have Award Runner Up.

[pdf] [arXiv] [code] [cite

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

  • Li L., Dominici, F., Blomberg, A. J., Bargagli Stoffi, F. J., Schwartz, J. D., Coull, B. A.,  Spengler, J. D, Wei, J., Koutrakis, J. L. P. (2022) 

Exposure to Unconventional Oil and Gas Development and All-cause Mortality in Medicare Beneficiaries. Nature Energy. 

[pdf] [paper] [code] [cite

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

  • Bargagli-Stoffi, F.J., Cevolani G., Gnecco, G. (2022)

Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory. Minds and Machines.

[paper] [cite]

  • 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.

[paper] [preprint] [cite]

  • Bargagli Stoffi, F. J., Riccaboni M., & Niederreiter, J. (2021)

Supervised Learning for the Prediction of Firm Dynamics, in S. Consoli, D. et al. (eds.) Data Science for Economics and Finance: Methodologies and Applications, Springer.

[paper] [preprint] [code] [cite]


  • Wasfy, J. & 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.

[paper] [cite]

  • 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]​

  • Bargagli Stoffi, F. J., Cevolani, G., & Gnecco, G. (2020)

Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? International Conference on Machine Learning, Optimization, and Data Science.

[paper] [cite]

  • Bargagli Stoffi, F. J., Gnecco, G. (2018)

Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms. IEEE 5th International Conference on Data Science and Advanced Analytics.​

[paper] [preprint] [cite]

Ongoing Projects

  • Bargagli-Stoffi, F. J., Tortù, C., & Forastiere, L. (2022+)

Heterogeneous Treatment and Spillover Effects under Clustered Network Interference. Submitted.

American Causal Inference Conference 2022 Tom Ten Have Award Runner Honorable Mention.[pdf[preprint] [cite]

  • Bargagli Stoffi, F. J., Riccaboni, M., Rungi, A. (2022+)

Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints. Submitted.

First Credit Scoring and Credit Rating Conference (CSCR I) Best Paper Award​.

[pdf] [preprint] [cite]

  • Lee, K., Bargagli Stoffi, F. J., & Dominici, F. (2022+)

Causal Rule Ensemble: Interpretable Inference on Heterogeneous Treatment Effects. Submitted.

[pdf[preprint]​​​​ [cite]

  • Bargagli Stoffi, F. J., De Beckker, K., De Witte, K., & Maldonado, J. (2022+)

Assessing Sensitivity of Machine Learning Predictions. A Novel Toolbox with an Application to Financial Literacy.Submitted.

[preprint] [code] [cite]

  • Byrne, S., Reynolds, A.P.F., Biliotti, C., Bargagli Stoffi, F.J., Polonio, L., Riccaboni, R. (2022+)
    Anticipating Choice Behaviour in Strategic Settings via Machine Learning Modeling of Scanpath Subsequences. Submitted.

  • Incerti, F., Bargagli Stoffi, F.J., Riccaboni, M. (2022+)
    Bayesian Machine Learning for Cross-country Prediction of Default Risk. Submitted