Causal Representation Learning via Counterfactual Diffusion Networks

Abstract

This paper introduces Counterfactual Diffusion Networks (CDN), a novel approach to causal representation learning. Traditional representation learning methods often fail to accurately capture underlying causal relationships within data, leading to issues in downstream tasks that rely on understanding these relationships. CDN addresses this limitation by employing a diffusion model to simulate counterfactual scenarios. The core idea is to learn representations that reflect how changes to one variable propagate through the system, effectively capturing causal dependencies. The diffusion process allows the network to learn robust representations even in the presence of noise and confounding variables. We demonstrate that CDN outperforms existing correlation-based methods in capturing causal structure and improves the accuracy of causal inference tasks. The key contributions of this work are the integration of diffusion models with counterfactual reasoning and the demonstration of CDN's effectiveness in learning causal representations.