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.

@article{schneider2024generative,
	author = {Nora Schneider and Lars Lorch and Niki Kilbertus and Bernhard Sch{\"o}lkopf and Andreas Krause},
	journal = {arXiv preprint arXiv:2411.14003},
	pdf = {https://arxiv.org/pdf/2411.14003},
	title = {Generative Intervention Models for Causal Perturbation Modeling},
	month = {November},
	year = {2024}}