I am a PhD candidate at the Institute of AI in Management, LMU Munich. I do causal machine learning research under the supervision of prof. Stefan Feuerriegel.

I believe in the data-driven automation and human-computer cooperation – they should become the main booster of sustainable development and inclusive society.

My research interests include

• Causal Machine Learning / Treatment Effect Estimation
• Causal Inference
• Probabilistic Modelling

News

2024

• 👨‍🏫 [Oct 2024] Designated as a co-director of a newly founded Causal ML Lab at the Institute of AI in Management, LMU Munich
• 🎤 [Oct 2024] Holding a guest lecture ‘Causal machine learning for predicting treatment outcomes’ at the Online Machine Learning School 2024
• 📙 [Sep 2024] 2 papers accepted at NeurIPS 2024: Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner (poster), DiffPO: A causal diffusion model for predicting potential outcomes of treatments (poster)
• 🎤 [Aug 2024] Presenting a tutorial ‘Causal ML for treatment effect estimation’ at the 3rd Munich Causal ML Workshop
• ✈️ [Jul 2024] Attending ICML 2024 in Vienna, Austria
• 🧘‍ [Jul 2024] Attending relAI retreat 2024 & presenting Nature Medicine paper ‘Causal machine learning for predicting treatment outcomes’ in Miesbach, Germany
• 🎤 [Jun 2024] Presenting Nature Medicine paper ‘Causal machine learning for predicting treatment outcomes’ at the Tissue Image Analytics (TIA) seminar (University of Warwick)
• ✈️ [May 2024] Attending ICLR 2024 in Vienna, Austria
• 📙 [May 2024] 1 paper accepted at ICML 2024: Fair Off-Policy Learning from Observational Data
• 📙 [Apr 2024] A paper published in Nature Medicine: Causal Machine Learning for Predicting Treatment Outcomes
• 🎓 [Feb 2024 - May 2024] Attending van der Schaar Lab at the University of Cambridge as a visiting PhD student
• 📙 [Jan 2024] 3 papers accepted at the ICLR 2024: A Neural Framework for Generalized Causal Sensitivity Analysis (poster); Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation (poster); Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation (spotlight)
• 🎤 [Jan 2024] Presenting a pre-print ‘Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation’ at the 2nd Munich Causal ML Workshop

2023

• 🏅 [Nov 2023] Designated as a top reviewer at NeurIPS 2023
• 🧘‍ [Nov 2023] Attending relAI retreat 2023 in Chiemsee, Germany
• 💼 [Oct 2023] Attending Zuse Schools autumn event 2023 in Dresden, Germany
• 📙 [Sep 2023] 3 papers accepted at NeurIPS 2023: Sharp Bounds for Generalized Causal Sensitivity Analysis (poster), Reliable Off-Policy Learning for Dosage Combinations (poster), Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model (spotlight)
• ✈️ [Jul 2023] Attending ICML 2023 at Honolulu, Hawaii, USA
• 🏫 [Jul 2023] Attending Munich Econometrics Workshop 2023 in Munich, Germany
• 🏫 [Jun 2023] Attending Nordic Probabilistic AI School 2023 as a participant and teacher assistant
• 🚀 [Jun 2023] Joined Konrad Zuse School of Excellence in Reliable AI (relAI) as an Associated PhD student
• 🎤 [May 2023] Presenting ICML 2022 & 2023 papers at the 1st Munich Causal ML Workshop
• 🏅 [Apr 2023] Designated as a top reviewer at AISTATS 2023
• 📙 [Apr 2023] 1 paper accepted at ICML 2023: Normalizing Flows for Interventional Density Estimation
• 🎤 [Feb 2023] Presenting an ICML 2022 paper ‘Causal Transformer for Estimating Counterfactual Outcomes’ at the Causality Discussion Group

2022

• 📙 [Nov 2022] 1 paper accepted at AAAI 2023: Estimating Average Causal Effects from Patient Trajectories
• 📙 [May 2022] 1 paper accepted at ICML 2022: Causal Transformer for Estimating Counterfactual Outcomes

2021

• 🚀 [Jun 2021] Started a PhD under the supervision of prof. Stefan Feuerriegel at the LMU Munich