TBNAI
Transporting the Past: An Optimal Transport View of Backtracking Counterfactuals
Publication: cs.AI updates on arXiv.org
Author: Zhangyi Liu, Pouria Fatemi, Suvrit Sra
Source published: 09/11/2026, 05:00:00 UTC
Briefing created: 09/11/2026, 07:20:56 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/11/2026, 05:03:25 UTC.
Briefings
A new study proposes optimal transport frameworks to solve backtracking counterfactual inference in Structural Causal Models (SCMs). The research introduces two complementary methods: a natural pointwise projection for direct transport and a mutual independence enforcement to ensure exogenous variables remain interpretable as genuine noise. Both approaches align with the original backtracking principle and offer solvers for finite-posterior settings. Synthetic experiments on non-bijective SCMs demonstrate the geometry of the problem and highlight trade-offs between transport cost, constraint satisfaction, and independence preservation.
Key points
- The study introduces two complementary frameworks for backtracking counterfactual inference in Structural Causal Models (SCMs) using optimal transport.
- Both frameworks are consistent with the original backtracking counterfactual principle and offer solvers for finite-posterior settings.
Context
This research addresses the challenge of inferring counterfactuals in SCMs, where a factual observation induces a posterior distribution over exogenous variables. The study provides new methods to transport this posterior onto exogenous configurations satisfying a desired counterfactual query.
Limitations
Evidence is limited to a single arXiv preprint (arXiv:2611.08682v1) without peer review or empirical validation. Results are based on synthetic experiments on non-bijective SCMs and do not include real-world data or causal effect estimates.
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