DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
DiffusionCounterfactuals：因果表征引导的高维反事实推断
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…