Causal Machine Learning

In the recent years, there has been a surging interest in the use of Statistical/Machine Learning (ML) tools for causal inference. These tools can leverage large datasets and usually deliver excellent predictive performance. However they need to be properly adjusted to be used in causal settings (e.g. to account for confounding bias).

The broad idea of this project is to design and develop Causal ML methods for estimating individual treatment effects, and for policy evaluation/optimization (Reinforcement Learning), with observational data.