Abstract

Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various domains, including healthcare, economics, and social sciences. However, existing methods often struggle to generate accurate and consistent counterfactuals, particularly when the causal relationships are complex. We propose a novel framework that incorporates causal mechanisms and diffusion models to generate high-quality counterfactual samples guided by causal representation. Our approach introduces a novel, theoretically grounded training and sampling process that enables the model to consistently generate accurate counterfactual high-dimensional data under multiple intervention steps. Experimental results on various synthetic and real benchmarks demonstrate the proposed approach outperforms state-of-the-art methods in generating accurate and high-quality counterfactuals, using different evaluation metrics.

Metadata

publication

arXiv preprint arXiv:2407.20553, 2024

year

2024

publication date

2024/7/30

authors

Jiageng Zhu, Hanchen Xie, Jiazhi Li, Wael Abd-Almageed

link

https://arxiv.org/abs/2407.20553

resource_link

https://arxiv.org/pdf/2407.20553

journal

arXiv preprint arXiv:2407.20553