Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is difficult. In this work, we propose to amortize causal structure learning. Rather than searching over structures, we train a variational inference model to predict the causal structure from observational or interventional data. This allows us to bypass both the search over graphs and the hand-engineering of suitable score functions. Instead, our inference model acquires domain-specific inductive biases for causal discovery solely from data generated by a simulator. The architecture of our inference model emulates permutation invariances that are crucial for statistical efficiency in structure learning, which facilitates generalization to significantly larger problem instances than seen during training. On synthetic data and semisynthetic gene expression data, our models exhibit robust generalization capabilities when subject to substantial distribution shifts and significantly outperform existing algorithms, especially in the challenging genomics domain. Our code and models are publicly available at: https://github.com/larslorch/avici.

翻译：推断因果结构会产生一个组合式的搜索问题, 通常涉及通过评分或独立测试来评估结构。 结果的搜索成本很高, 并且设计适当的评分或测试来捕捉先前的知识是困难的。 在这项工作中, 我们建议对因果结构进行分解学习。 我们不研究结构, 而是训练一个变式推论模型来预测从观察或干预数据得出的因果结构。 这使我们能够绕过对图的搜索和适当评分功能的手动工程。 相反, 我们的推论模型只从模拟器生成的数据中获取特定域的诱导偏差, 导致因果发现。 我们的推论模型的结构模仿结构的变异性对于结构学习中的统计效率至关重要, 这有助于将问题普遍化到比培训期间要大得多的情况。 在合成数据和半合成基因表达数据方面, 我们的模型在面临大幅分布变化和显著超出现有算法时, 特别是在具有挑战的基因组学域中, 。 我们的代码和模型公开提供: https://github.com/larlorsch/ avicci。