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I am Riccardo 🇮🇹, an enthusiastic Ph.D. Student at Causal Learning and Artificial Intelligence Lab (ISTA) advised by Francesco Locatello and co-advised by Cordelia Schmid.
Through my research, I aim to revisit general representation learning objectives when interested in causal downstream tasks (e.g., Treatment Effect Estimation) in order to (i) improve experiments' efficiency and (ii) provide trustworthy guarantees to AI-powered scientific discovery. Currently testing several applications in experimental ecology, and even more interested in applications in healthcare.
Here you can find a collection of different packages, challenges and problems regarding:
Machine Learning 📈,
Optimization 🎯,
Statistics 📊,
Causal Inference ⁉️,
that I have solved and implemented during the last few years and I can share publicly 🔓.
For a ordered archive of these projects see: MY PORTFOLIO 📁 and don't hesitate to reach out at riccardo.cadei@ist.ac.at 📫 for any constructive discussion.
Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)
