Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experimental or interventional conditions. BaCaDI is fully differentiable, which allows us to infer the complex joint posterior over the intervention targets and the causal structure via efficient gradient-based variational inference. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.

翻译：从实验中推断因果关系是许多领域的核心任务。例如在生物学中，最近的进展使得我们能够在药物或基因敲除等多种干预条件下获取单细胞表达数据。然而，干预的目标通常不确定或未知，且观测数量有限。因此，标准的因果发现方法无法再可靠地使用。为弥补这一不足，我们提出了一个贝叶斯框架（BaCaDI），用于发现和推理在各种未知实验或干预条件下生成的数据背后的因果结构。BaCaDI完全可微，这使得我们能够通过高效的基于梯度的变分推断来推断干预目标和因果结构的复杂联合后验。在合成因果发现任务和模拟基因表达数据的实验中，BaCaDI在识别因果结构和干预目标方面优于相关方法。