We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available. Utilizing the recent developments in diffusion models, we introduce diffusion-based causal models (DCM) to learn causal mechanisms, that generate unique latent encodings to allow for direct sampling under interventions as well as abduction for counterfactuals. We utilize DCM to model structural equations, seeing that diffusion models serve as a natural candidate here since they encode each node to a latent representation, a proxy for the exogenous noise, and offer flexible and accurate modeling to provide reliable causal statements and estimates. Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries. Our theoretical results provide a methodology for analyzing the counterfactual error for general encoder/decoder models which could be of independent interest.

翻译：我们考虑在仅具有观测数据和因果图的因果充分设定中，回答观测性、干预性和反事实查询的问题。利用扩散模型的最新进展，我们引入基于扩散的因果模型（DCM）来学习因果机制，生成独特的潜在编码，从而允许在干预下直接采样以及通过溯因进行反事实推理。我们利用DCM对结构方程进行建模，发现扩散模型在此处是天然候选方案，因为它能将每个节点编码为潜在表示（作为外生噪声的代理），并提供灵活且准确的建模能力，以产生可靠的因果陈述和估计。我们的实证评估表明，在回答因果查询方面，该方法显著优于现有最先进方法。我们的理论结果为分析通用编码器/解码器模型的反事实误差提供了方法论，该成果可能具有独立的研究价值。