Centre of Competence for Data Science and Simulation is a cross-departmental initiative. Our ambition is to develop infrastructure, tools and competences in machine learning, big data, microsimulation and geoexperimentation to conduct excellent research and advise policy-makers as well as stakeholders

Under the series of discussions around data science we host:

·        Validation of Machine Learning Estimated Conditional Average Treatment Effects

Chair:            Michael Knaus (University of Tübingen)

When:            Wednesday, 12 February 2025 from 12:30 to 13:30

Where:           LISER Salle Conference, 1st Floor (Jane Jacobs room)

Summary:

Numerous causal machine learning estimators are available for estimating conditional average treatment effects (CATEs). This paper reviews methods for testing whether these estimators detect genuine systematic effect heterogeneity or merely produce sophisticated noise. We present a unifying theoretical framework that encompasses various approaches in the literature, such as Generic ML and rank-weighted treatment effects, as special cases. Using both simulated and real-world datasets, we evaluate the statistical power of these methods, offering practical guidance for researchers and practitioners seeking to validate their CATE models..