Agenda

Title: Causal ML for predicting treatment outcomes

Abstract: Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes, with large potential for personalizing decision-making in medicine and management. Here, we explore recent advances in Causal ML and their relevance for translation in medicine and business decision-making, often motivated by practical considerations. (1) Many of the existing Causal ML methods are aimed at more standard settings but several, specialized settings in practice have been explored only recently (e.g., Causal ML for dosage combinations, Causal ML for time-series settings). (2) Causal ML often generates only point estimates while decision-making, especially in medicine, requires uncertainty estimates. (3) Causal ML rests on formal assumptions, which typically cannot be tested. Here, new methods such as in causal sensitivity analysis help improve the reliability in real-world settings.

Short bio: Valentyn Melnychuk is a 4th-year PhD student at LMU Munich, supervised by Prof. Stefan Feuerriegel, and co-director of the Causal ML Lab at the Institute of AI in Management. Previously, he was a Research Assistant at the Fraunhofer Institute for Integrated Circuits IIS in Munich. His research focuses on Causal Machine Learning, Causal Inference, and Probabilistic Modeling, topics on which he has co-authored scientific publications in top conferences and journals such as ICML, AAAI, NeurIPS, ICLR, and Nature Medicine. More information at https://valentyn1997.github.io/ and https://valentyn1997.github.io/cv/

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