Dennis Frauen

Dennis Frauen

PhD Student in Causal Machine Learning

Dennis Frauen

I am a final year PhD student at LMU Munich, where I am also co-director of the CausalML Lab. I am part of the ELLIS PhD program, where I am co-supervised by Stefan Feuerriegel (LMU Munich) and Mihaela van der Schaar (University of Cambridge).

I am on the 2025 job market and open to internship/full-time opportunities in both industry and academia.

My research centers on building the next generation of AI/ML methods for reliable data-driven decision-making. Specifically, I focus on designing methods that not only predict outcomes but also understand the (causal) impact of actions—a critical step towards truly reliable decision-making. Central to my work are three key aspects: Optimality/Efficiency (designing models and algorithms that maximally leverage the information from the available data), Robustness (ensuring stable performance under distributional shifts, incomplete information, or violations of assumptions), and Trustworthiness (building transparent models that account for ethical considerations and social impact). More specifically, my research interests span:

  • Causal machine learning, particularly treatment effect estimation
  • Off-policy learning and reinforcement learning
  • Robustness of causal inference methods (e.g., sensitivity analysis or partial identification)
  • Uncertainty quantification (e.g., conformal prediction or Bayesian methods)
  • (Causal) algorithmic fairness
  • Foundation models / LLMs for causal inference
  • Applications in various fields, including medicine and economics

News!

[23/01/2025] I am excited to join the van der Schaar lab at the University of Cambridge as a visiting PhD student for the next three weeks as part of the ELLIS PhD program. If you are in the Cambridge/ London area and would like to meet for a coffee, let me know!

[22/01/2025] Two accepted papers at ICLR 2025! Model-Agnostic Meta-Learners for Estimating Heterogeneous Treatment Effects over Time and Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets.

[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.