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We consider the problem of answering observational, interventional, andcounterfactual queries in a causally sufficient setting where onlyobservational data and the causal graph are available. Utilizing the recentdevelopments in diffusion models, we introduce diffusion-based causal models(DCM) to learn causal mechanisms, that generate unique latent encodings. Theseencodings enable us to directly sample under interventions and performabduction for counterfactuals. Diffusion models are a natural fit here, sincethey can encode each node to a latent representation that acts as a proxy forexogenous noise. Our empirical evaluations demonstrate significant improvementsover existing state-of-the-art methods for answering causal queries.Furthermore, we provide theoretical results that offer a methodology foranalyzing counterfactual estimation in general encoder-decoder models, whichcould be useful in settings beyond our proposed approach.

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries