Generative Intervention Models for Causal Perturbation Modeling

• URL: http://arxiv.org/abs/2411.14003v1
• Date: Thu, 21 Nov 2024 10:37:57 GMT
• Title: Generative Intervention Models for Causal Perturbation Modeling
• Authors: Nora Schneider, Lars Lorch, Niki Kilbertus, Bernhard Schölkopf, Andreas Krause,
• Abstract summary: In many applications, it is a priori unknown which mechanisms of a system are modified by an external perturbation. We propose a generative intervention model (GIM) that learns to map these perturbation features to distributions over atomic interventions.
• Score: 80.72074987374141
• License:
• Abstract: 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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