Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries

• URL: http://arxiv.org/abs/2302.00860v3
• Date: Wed, 09 Oct 2024 18:04:37 GMT
• Title: Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
• Authors: Patrick Chao, Patrick Blöbaum, Sapan Patel, Shiva Prasad Kasiviswanathan,
• Abstract summary: We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting. We introduce diffusion-based causal models (DCM) to learn causal mechanisms, that generate unique latent encodings. Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries.
• Score: 10.818661865303518
• License:
• 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.

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