Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
利用扩散模型刻画因果机制并回答干预与反事实问题
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. These encodings enable us to directly sample under interventions and perform abduction for counterfactuals.…