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

这篇论文探讨了利用自回归流模型进行因果推断任务，包括因果关系发现、干预预测和反事实预测，并提出了一种新的流结构来执行所有三个任务，该方法在综合数据和基准数据集上得到了有效验证。