Interventional and Counterfactual Inference with Diffusion Models
- URL: http://arxiv.org/abs/2302.00860v2
- Date: Tue, 6 Jun 2023 22:43:39 GMT
- Title: Interventional and Counterfactual Inference with Diffusion Models
- Authors: Patrick Chao, Patrick Bl\"obaum, 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: 9.87466705221632
- License: http://creativecommons.org/licenses/by/4.0/
- 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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