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DiffPO: A Causal Diffusion Model for Learning Distributions of Potential Outcomes

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Link to Profile Stefan Feuerriegel PI Matchmaking

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Abstract

Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance.

inproceedings

NeurIPS 2024

38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024.
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A* Conference

Authors

Y. Ma • V. Melnychuk • J. Schweisthal • S. Feuerriegel

Links

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Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: MMS+24

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