Guest lecture: Causal ML for predicting treatment outcomes

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.

Paper Link: https://www.nature.com/articles/s41591-024-02902-1