We study how to extend the use of the diffusion model to answer the causal question from the observational data under the existence of unmeasured confounders. In Pearl's framework of using a Directed Acyclic Graph (DAG) to capture the causal intervention, a Diffusion-based Causal Model (DCM) was proposed incorporating the diffusion model to answer the causal questions more accurately, assuming that all of the confounders are observed. However, unmeasured confounders in practice exist, which hinders DCM from being applicable. To alleviate this limitation of DCM, we propose an extended model called Backdoor Criterion based DCM (BDCM), whose idea is rooted in the Backdoor criterion to find the variables in DAG to be included in the decoding process of the diffusion model so that we can extend DCM to the case with unmeasured confounders. Synthetic data experiment demonstrates that our proposed model captures the counterfactual distribution more precisely than DCM under the unmeasured confounders.

翻译：我们研究如何在存在未测量混杂因素的情况下，扩展扩散模型以基于观测数据回答因果问题。在Pearl提出的利用有向无环图（DAG）刻画因果干预的框架中，一种名为基于扩散的因果模型（DCM）的方法被提出，其通过整合扩散模型来更准确地回答因果问题，前提是假设所有混杂因素均被观测到。然而，现实中存在的未测量混杂因素限制了DCM的适用性。为缓解DCM的这一局限性，我们提出了一种扩展模型——基于后门准则的DCM（BDCM），其核心思想源于后门准则，通过寻找DAG中需纳入扩散模型解码过程的变量，使得DCM能够推广至存在未测量混杂因素的情形。合成数据实验表明，在未测量混杂因素存在时，我们提出的模型能比DCM更精确地捕获反事实分布。