# 运输过去：最优传输视角下的回溯反事实

> 全文为英语

**Published:** 2026年11月9日
**Author:** Zhangyi Liu, Pouria Fatemi, Suvrit Sra
**Source:** arXiv — cs.AI
**Language:** en-US
**HTML:** https://aiqazaqstan.com/zh/articles/transporting-the-past-an-optimal-transport-view-of-backtracking-counterf

arXiv:2611.08682v1 发布类型：新 摘要：我们通过最优传输的视角研究结构因果模型（SCM）的回溯反事实推断。在SCM中，事实观察通常会在外生变量上诱导出后验分布，这使得...

arXiv:2611.08682v1 Announce Type: new Abstract: We study backtracking counterfactual inference for Structural Causal Models (SCMs) through the lens of optimal transport. In SCMs, a factual observation generally induces a posterior distribution over exogenous variables, making counterfactual inference inherently distributional. We introduce two complementary frameworks that transport this posterior onto the set of exogenous configurations satisfying a desired counterfactual query. The first constructs a natural pointwise projection and is suitable when the transported exogenous distribution can be used directly. The second enforces mutual independence among counterfactual exogenous variables, so that the transported variables remain interpretable as genuine exogenous noises of the same SCM. We show that both frameworks are consistent with the original backtracking counterfactual principle, and we propose marginal, penalty-based, and hard-constrained solvers for finite-posterior settings. Synthetic experiments on non-bijective SCMs illustrate the geometry of the problem and reveal trade-offs among transport cost, counterfactual constraint satisfaction, and preservation of exogenous independence.

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*内容来自原始来源： [arXiv — cs.AI](https://arxiv.org/abs/2611.08682). 查看原文.*

**标签：** #arXiv

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