Causal Machine Learning Workshop SEW-HSG University of St.Gallen

Program: Monday Session I Maximilian Kasy, "Adaptive treatment assignment in experiments for policy choice" Bezirgen Veliyev, "Functional Sequential Treatment Allocation" Keynote Uri Shalit about "Machine learning and causal inference: a two-way road": "This talk will have two parts. In the first we will discuss a framework we developed for learning individualized treatment recommendations from observational health data, merging ideas from machine learning and causal inference. We will see examples of our framework applied to two crucial health problems using data from tens of thousands of patients, and discuss some important causal-inference challenges that come to focus in this setting. In the second part we will show how we use ideas from the causal inference literature to address long standing problems in machine learning: off-policy evaluation in a partially observable Markov decision process (POMDP), and learning predictive models that are stable against distributional shifts." Heterogeneous effects of training programmes for unemployed in Belgium" Daniel Jacob, "Does Tenure make you love your Job?" Nicolaj Mühlbach, "Heterogeneous Treatment Effects of an Early Retirement Reform" Tuesday Session III Dmitry Arkhangelsky, "Double-Robust Identification for Causal Panel Data Models" Martin Spindler, "Uniform Inference in High-Dimensional Gaussian Graphical Models" Keynote Stefan Wager about "Designing Loss Functions for Causal Machine Learning": "Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible.