Dennis Frauen
I am a final year PhD student at the Institute of AI in Management, LMU Munich, supervised by Prof. Stefan Feuerriegel. I am also an Ellis PhD student, co-supervised by Prof. Mihaela van der Schaar (University of Cambridge) My research focuses mainly on causal inference and machine learning. In particular, I develop efficient, robust, and reliable machine learning methods for causal inference. Furthermore, I use ideas from causality to make data-driven decision-making optimal and fair.
Research interests
- Causal machine learning
- Uncertainty quantification
- Partial identification and sensitivity analysis
- Off-policy evaluation and reinforcement learning
- (Causal) algorithmic fairness
- Foundation models for causal inference
News!
[20/11/2024] I won the G-Research Early Career Grant. It will support my research stay with Mihaela van der Schaar at the University of Cambridge in January/February 2025.
[30/10/2024] We launched the CausalML lab at LMU Munich, where I will be co-director. The project is supported by the German Federal Ministry of Education and Research, in collaboration with Niki Kilbertus (Helmholtz/TUM), Stefan Bauer (Helmholtz/TUM), Nadja Klein (KIT), and EconomicAI.
[03/06/2024] I moved to the San Francisco Bay area to start my PhD internship at Netflix Research. I will be working on deep learning for message recommendations, leveraging tools from Causal ML and Reinforcement learning.
[16/05/2024] Our paper Causal Machine Learning for Cost-Effective Allocation of Development Aid was accepted at KDD 2024.
[02/05/2024] Two accepted papers at ICML 2024! Fair Off-Policy Learning from Observational Data and Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments (preprint coming soon).
[19/04/2024] Our perspective paper Causal machine learning for predicting treatment outcomes was published at Nature Medicine.
[16/01/2024] Four accepted papers at ICLR 2024! A Neural Framework for Generalized Causal Sensitivity Analysis, Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation, Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework, and Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation.
[01/11/2023] I received a mention as a top reviewer at NeurIPS 2023.
[26/09/2023] Together with Stefan and Abdu, we presented an overview of our research at the Ladenburger Diskurs.
[21/09/2023] Three accepted papers at NeurIPS 2023! Sharp Bounds for Generalized Causal Sensitivity Analysis, Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model, and Reliable Off-Policy Learning for Dosage Combinations.
[24/08/2023] I presented some of our ongoing work on Causal sensitivity analysis at Microsoft Research, Cambridge.
[11/06/2023] I am excited to join the van der Schaar lab at the University of Cambridge as a visiting PhD student this summer.
[24/04/2023] Our paper Normalizing Flows for Interventional Density Estimation was accepted at ICML 2023.
[22/01/2023] Our paper Estimating individual treatment effects under unobserved confounding using binary instruments was accepted at ICLR 2023.
[19/11/2022] Our paper Estimating average causal effects from patient trajectories was accepted at AAAI 2023.
[8/11/2022] I presented some of our ongoing work on Fair policy learning from observational data at the Causal Data Science Meeting 2022.