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Generative Intervention Models for Causal Perturbation Modeling.
In: (42nd International Conference on Machine Learning, ICML 2025, 13-19 July 2025, Vancouver). 2025. 53388-53412 (Proceedings of Machine Learning Research ; 267)
We consider the problem of predicting perturbation effects via causal models. In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation, even though the features of the perturbation are available. For example, in genomics, some properties of a drug may be known, but not their causal effects on the regulatory pathways of cells. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions in a jointly-estimated causal model. Contrary to prior approaches, this enables us to predict the distribution shifts of unseen perturbation features while gaining insights about their mechanistic effects in the underlying data-generating process. On synthetic data and scRNA-seq drug perturbation data, GIMs achieve robust out-of-distribution predictions on par with unstructured approaches, while effectively inferring the underlying perturbation mechanisms, often better than other causal inference methods.
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Publikationstyp
Artikel: Konferenzbeitrag
Konferenztitel
42nd International Conference on Machine Learning, ICML 2025
Konferzenzdatum
13-19 July 2025
Konferenzort
Vancouver
Quellenangaben
Band: 267,
Seiten: 53388-53412