Reliable causal machine learning for real-world data

Dennis Frauen (Ph.D. Student)

Causal inference from observational data (also called real-world data) is relevant in many disciplines, where randomized controlled trials are infeasible, e.g., due to ethical concerns or costs. Examples include medicine, economics, or marketing. However, observational data comes with many inherent challenges for causal inference, which are of both statistical and causal nature. This thesis aims to develop machine learning methods for reliable causal inference from observational data. Examples of planned/completed projects include proposing methods for efficient estimation of (heterogenous) treatment effects in complex inference settings but also developing methods to relax standard assumptions, such as allowing for unobserved confounding. The particular focus is on leveraging and adopting recent advances in deep learning for causal inference, which requires thinking about novel architectures, learning algorithms, and theory. In summary, the work aims at both theoretical and methodological advances, which should empower practitioners from a variety of disciplines to perform causal inference with rigorous guarantees in real-world scenarios.

Primary Host:	Stefan Feuerriegel (LMU Munich)
Exchange Host:	Mihaela van der Schaar (University of Cambridge, The Alan Turing Institute & University of California)
PhD Duration:	01 September 2021 - 01 June 2025
Exchange Duration:	01 June 2023 - 01 September 2023 01 October 2024 - 01 November 2024