Publications and Preprints
Heterogeneous Causal Effects with Imperfect Compliance: a Bayesian Machine Learning Approach.The Annals of Applied Statistics, forthcoming.
Atlantic Causal Inference Conference Tom Ten Have Award Runner Up
The First Credit Scoring and Credit Rating Conference (CSCR I) Best Paper Award
Bargagli Stoffi, F. J., De Beckker, K., De Witte, K., & Maldonado, J. (2021+)
Assessing Sensitivity of Machine Learning Predictions. A Novel Toolbox with an Application to Financial Literacy. Submitted.
From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science using a Potential Outcome Framework. Harvard Data Science Review. DOI: https://doi.org/10.1162/99608f92.8102afed.
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.
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
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.
Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? in Nicosia G. et al. (eds.) Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12565. Springer.
Estimating Heterogeneous Causal Effects in the Presence of Irregular Assignment Mechanisms. IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).
Smet, M., D’Inverno, G., Tierens, H., Bargagli Stoffi, F.J. and De Witte, K. (2021). The Effectiveness of the GOK Policy, in De Vos, G. and Tuytens, M. (eds.). Differences in Education. Striving for Excellence and Equal Educational Opportunities. Politeia.