DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
This addresses the challenge of generating accurate counterfactuals for decision-making in domains like healthcare and economics, representing a novel method for a known bottleneck.
The paper tackles the problem of accurately estimating counterfactual outcomes in high-dimensional data, such as in healthcare and economics, by proposing a framework that combines causal mechanisms and diffusion models to generate high-quality counterfactual samples, with experimental results showing it outperforms state-of-the-art methods on various benchmarks.
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.