This paper introduces a procedure for testing the identifiability of Bayesian models for causal inference. Although the do-calculus is sound and complete given a causal graph, many practical assumptions cannot be expressed in terms of graph structure alone, such as the assumptions required by instrumental variable designs, regression discontinuity designs, and within-subjects designs. We present simulation-based identifiability (SBI), a fully automated identification test based on a particle optimization scheme with simulated observations. This approach expresses causal assumptions as priors over functions in a structural causal model, including flexible priors using Gaussian processes. We prove that SBI is asymptotically sound and complete, and produces practical finite-sample bounds. We also show empirically that SBI agrees with known results in graph-based identification as well as with widely-held intuitions for designs in which graph-based methods are inconclusive.

中文翻译：

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贝叶斯因果推理可识别性的基于仿真的检验

本文介绍了一种用于检验因果推断的贝叶斯模型可识别性的过程。尽管在给定因果图的情况下演算是完善且完整的，但许多实际假设不能仅凭图结构来表示，例如工具变量设计，回归不连续性设计和对象内部设计所需的假设。我们提出基于仿真的可识别性（SBI），这是一种基于带有模拟观测值的粒子优化方案的全自动识别测试。这种方法将因果假设表示为结构因果模型中函数的先验，包括使用高斯过程的灵活先验。我们证明SBI渐近健全且完整，并产生实际的有限样本边界。