Causal Structure and Representation Learning with Biomedical Applications
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Abstract (arXiv)
Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep learning applications, as it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. Although representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of a perturbation/intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of multi-modal data (observational and perturbational, imaging-based and sequencing-based, at the single-cell level, tissue-level, and organism-level). We outline a statistical and computational framework for causal structure and representation learning motivated by fundamental biomedical questions: how to effectively use observational and perturbational data to perform causal discovery on observed causal variables; how to use multi-modal views of the system to learn causal variables; and how to design optimal perturbations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 81 references that are in the map. First posted on arXiv 6 Nov 2025.
Builds on
23 of 81 references.
References · 23
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- 2022Interventional Causal Representation Learningcited by 140
- 2018Invariance, Causality and Robustnesscited by 123
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- 2013Causal Discovery from Changescited by 52
11 more
- 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
- 2013Learning Sparse Causal Models is not NP-hardcited by 48
- 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
- 2023Unpaired Multi-Domain Causal Representation Learningcited by 32
- 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
- 2021Matching a Desired Causal State via Shift Interventionscited by 16
- 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modelingcited by 15
- 2024Causal Discovery with Fewer Conditional Independence Testscited by 13
- 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Datacited by 7
- 2019Multi-Domain Translation by Learning Uncoupled Autoencoderscited by 3
- 2023Meek Separators and Their Applications in Targeted Causal Discoverycited by 3
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Citing papers per year: 2026: 3.
3 papers.