提出扩散模型DiffPO，可学习干预结果的分布并量化不确定性
DiffPO: A causal diffusion model for learning distributions of potential outcomes
- 用定制扩散模型学习潜在结果的分布，解决选择偏差问题
- 在多种实验中表现优于现有方法，实现最先进的预测性能
- 适用于医学决策等需要不确定性评估的场景
从观测数据中预测干预的潜在结果对医疗决策至关重要，但受因果推断基本难题制约。现有方法多仅提供点估计且缺乏不确定性量化，忽略了潜在结果分布的完整信息。本文提出新型因果扩散模型DiffPO，通过定制的条件去噪扩散机制学习复杂分布，并引入正交扩散损失缓解选择偏差。该方法具有高度灵活性，还可用于估计其他因果量（如CATE）。在广泛实验中，DiffPO均达到领先性能。
原文摘要 · Abstract (English)
Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are largely limited to point estimates of potential outcomes with no uncertain quantification; thus, the full information about the distributions of potential outcomes is typically ignored. In this paper, we propose a novel causal diffusion model called DiffPO, which is carefully designed for reliable inferences in medicine by learning the distribution of potential outcomes. In our DiffPO, we leverage a tailored conditional denoising diffusion model to learn complex distributions, where we address the selection bias through a novel orthogonal diffusion loss. Another strength of our DiffPO method is that it is highly flexible (e.g., it can also be used to estimate different causal quantities such as CATE). Across a wide range of experiments, we show that our method achieves state-of-the-art performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。