DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations

TL;DR

This paper introduces a new framework combining causal representations and diffusion models to accurately generate high-dimensional counterfactuals, improving over existing methods in complex causal scenarios.

Contribution

It presents a novel, theoretically grounded approach that effectively incorporates causal mechanisms into diffusion models for high-dimensional counterfactual inference.

Findings

Outperforms state-of-the-art methods on synthetic benchmarks

Generates accurate counterfactuals under multiple interventions

Demonstrates robustness in real-world datasets

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.