用生成模型预测未知干预机制，揭示药物对细胞通路的影响
Generative Intervention Models for Causal Perturbation Modeling
- 将扰动特征映射到因果模型的原子干预分布
- 在合成数据和单细胞测序数据上实现强泛化预测性能
- 适合研究药物作用机制或需要解释性因果推断的场景
我们研究通过因果模型预测外部扰动的效果。在许多应用中，尽管已知扰动的特征，但其作用机制尚不明确，例如在基因组学中，药物属性可知，但其对细胞调控通路的因果影响未知。本文提出生成干预模型（GIM），学习将扰动特征映射到联合估计的因果模型中的原子干预分布。与以往方法不同，该方法可在未见扰动特征下预测分布变化，并揭示其在数据生成过程中的机制。在合成数据和scRNA-seq药物扰动数据上，GIM在分布外预测表现与无结构方法相当，同时有效推断底层扰动机制，通常优于其他因果推断方法。
原文摘要 · Abstract (English)
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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