Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries

Patrick Chao, Patrick Blöbaum, Sapan Kirit Patel, Shiva Kasiviswanathan

Abstract: 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. Diffusion models are a natural fit here, since they can encode each node to a latent representation that acts as a proxy for exogenous noise. Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries. Furthermore, we provide theoretical results that offer a methodology for analyzing counterfactual estimation in general encoder-decoder models, which could be useful in settings beyond our proposed approach.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: Camera ready version taking into account the feedback from all the reviewers

Code: https://github.com/patrickrchao/DiffusionBasedCausalModels

Assigned Action Editor: ~Varun_Kanade1

Submission Number: 2909