| Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects FJ Bargagli-Stoffi, R Cadei, K Lee, F Dominici arXiv preprint arXiv:2009.09036, 2024 | 56 | 2024 |
| Simple Models in Complex Worlds: Occam’s Razor and Statistical Learning Theory FJ Bargagli Stoffi, G Cevolani, G Gnecco Minds and Machines 32 (1), 13-42, 2022 | 40 | 2022 |
| Heterogeneous Treatment and Spillover Effects under Clustered Network Interference FJ Bargagli Stoffi, C Tortú, L Forastiere arXiv preprint arXiv:2008.00707, 2023 | 34 | 2023 |
| Causal tree with instrumental variable: an extension of the causal tree framework to irregular assignment mechanisms FJ Bargagli Stoffi, G Gnecco International Journal of Data Science and Analytics 9 (3), 315-337, 2020 | 34 | 2020 |
| Heterogeneous causal effects with imperfect compliance: a Bayesian machine learning approach FJ Bargagli-Stoffi, K De-Witte, G Gnecco The Annals of Applied Statistics 16 (3), 1986-2009, 2022 | 33* | 2022 |
| Supervised learning for the prediction of firm dynamics FJ Bargagli-Stoffi, J Niederreiter, M Riccaboni Data Science for Economics and Finance: Methodologies and Applications, 19-41, 2021 | 31 | 2021 |
| From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework F Dominici, FJ Bargagli-Stoffi, F Mealli Harvard Data Science Review, 2022 | 24 | 2022 |
| Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries L Li, F Dominici, AJ Blomberg, FJ Bargagli-Stoffi, JD Schwartz, BA Coull, ... Nature Energy 7 (2), 177-185, 2022 | 20 | 2022 |
| Estimating heterogeneous causal effects in the presence of irregular assignment mechanisms FJ Bargagli Stoffi, G Gnecco 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018 | 20 | 2018 |
| Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values FJ Bargagli Stoffi, M Riccaboni, A Rungi Industrial and Corporate Change, 2023 | 14* | 2023 |
| Predicting choice behaviour in economic games using gaze data encoded as scanpath images SA Byrne, APF Reynolds, C Biliotti, FJ Bargagli-Stoffi, L Polonio, ... Scientific Reports 13 (1), 4722, 2023 | 10 | 2023 |
| Assessing sensitivity of machine learning predictions. a novel toolbox with an application to financial literacy FJ Bargagli-Stoffi, K De Beckker, JE Maldonado, K De Witte arXiv preprint arXiv:2102.04382, 2021 | 9* | 2021 |
| Confounder-dependent Bayesian mixture model: Characterizing heterogeneity of causal effects in air pollution epidemiology D Zorzetto, FJ Bargagli-Stoffi, A Canale, F Dominici Biometrics 80 (2), ujae025, 2024 | 5 | 2024 |
| Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning? FJ Bargagli-Stoffi, G Cevolani, G Gnecco International Conference on Machine Learning, Optimization, and Data Science …, 2020 | 4 | 2020 |
| Air quality disparities mapper: An open-source web application for environmental justice E McFarlin*, FJ Bargagli-Stoffi*, E Castro, J Schwartz, F Dominici, H Amini Environmental Modelling & Software, 105881, 2024 | 1 | 2024 |
| What is essential is visible to the eye: Saliency in primary school ranking and its effect on academic achievements FX Ladant, J Hedou, P Sestito, FJ Bargagli-Stoffi arXiv preprint arXiv:2302.10026, 2023 | 1* | 2023 |
| A Two-Country Study of Default Risk Prediction Using Bayesian Machine-Learning F Incerti, FJ Bargagli-Stoffi, M Riccaboni International Conference on Machine Learning, Optimization, and Data Science …, 2022 | 1 | 2022 |
| Assessing the Value of Echocardiography in the Absence of Randomized Trials: How Analytic Techniques from Causal Inference Can Fill the Gap JH Wasfy, FJ Bargagli-Stoffi Journal of the American Society of Echocardiography, 2021 | 1 | 2021 |
| Bayesian Nonparametrics for Principal Stratification with Continuous Post-Treatment Variables D Zorzetto, A Canale, F Mealli, F Dominici, FJ Bargagli-Stoffi arXiv preprint arXiv:2405.17669, 2024 | | 2024 |
| Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health KL Chen, FJ Bargagli-Stoffi, RC Kim, LRF Henneman, RC Nethery arXiv preprint arXiv:2404.13442, 2024 | | 2024 |
Francesca DominiciProfessor of Biostatistics在 hsph.harvard.edu 的电子邮件经过验证
