论文标题
基于血流的自回归因果发现和推理
Autoregressive flow-based causal discovery and inference
论文作者
论文摘要
我们认为，自回旋流程模型非常适合执行一系列因果推理任务 - 从因果发现到做出介入和反事实预测。特别是，我们利用了一个事实，即自回归体系结构定义了与因果订购类似的变量的排序，以便提出单个流程体系结构以执行上述所有三个任务。我们首先利用以下事实：流程模型估计数据的标准化对数密度，以根据似然比得出因果方向的双变量度量。尽管因果方向的传统措施通常需要对因果关系性质（例如线性）的性质进行限制性假设，但流程模型的灵活性允许任意因果关系依赖性。我们的方法与综合数据以及因果关系对基准数据集的替代方法进行了比较。随后，我们证明了流的可逆性自然允许直接评估介入和反事实预测，这分别需要在潜在变量上进行边缘化和条件。我们列出了典型数据的示例
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions. In particular, we exploit the fact that autoregressive architectures define an ordering over variables, analogous to a causal ordering, in order to propose a single flow architecture to perform all three aforementioned tasks. We first leverage the fact that flow models estimate normalized log-densities of data to derive a bivariate measure of causal direction based on likelihood ratios. Whilst traditional measures of causal direction often require restrictive assumptions on the nature of causal relationships (e.g., linearity),the flexibility of flow models allows for arbitrary causal dependencies. Our approach compares favourably against alternative methods on synthetic data as well as on the Cause-Effect Pairs bench-mark dataset. Subsequently, we demonstrate that the invertible nature of flows naturally allows for direct evaluation of both interventional and counterfactual predictions, which require marginalization and conditioning over latent variables respectively. We present examples over synthetic data where autoregressive flows, when trained under the correct causal ordering, are able to make accurate interventional and counterfactual predictions
