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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

I am a post-doctoral researcher at Harvard University where I am mentored by Prof. Francesca Dominici. In my work, I develop and combine data science methods in causal inference and artificial intelligence to address real-world problems in health and social sciences. My research is partially supported by the Harvard Data Science Postdoctoral Research Fund Award.

I hold a joint Ph.D. in Economics and Data Science at KU Leuven (Belgium) and IMT School for Advanced Studies (Italy). Before the Ph.D., I got degrees in Statistics and Sociology at University of Florence (Italy) and was awarded an Erasmus+ scholarship to study at Humboldt University of Berlin (Germany). Between 2013 and 2018, I served as a city councillor in my home town

This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology outperforms other machine learning techniques tailored for causal inference in discovering and estimating the heterogeneous causal effects while controlling for the familywise error rate (or - less stringently - for the false discovery rate) at leaves' level. BCF-IV sheds a light on the heterogeneity of causal effects in instrumental variable scenarios and, in turn, provides the policy-makers with a relevant tool for targeted policies. Its empirical application evaluates the effects of additional funding on students' performances. The results indicate that BCF-IV could be used to enhance the effectiveness of school funding on students' performance.

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