Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
This work addresses causal inference for researchers and practitioners, offering a novel method that is incremental in applying diffusion models to a known bottleneck in causal query answering.
The authors tackled the problem of answering observational, interventional, and counterfactual queries using only observational data and a causal graph, by introducing diffusion-based causal models (DCM) that learn causal mechanisms with unique latent encodings, resulting in significant improvements over state-of-the-art methods.
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.