mlsanityData to 14 Oct 2026

Graph Distance Based on Cause-Effect Estimands with Latents

arXiv 2510.25037 ↗First posted 28 Oct 2025cs.LGcited by 0

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Abstract (arXiv)

Causal discovery aims to recover graphs that represent causal relations among given variables from observations, and new methods are constantly being proposed. Increasingly, the community raises questions about how much progress is made, because properly evaluating discovered graphs remains notoriously difficult, particularly under latent confounding. We propose a graph distance measure for acyclic directed mixed graphs (ADMGs) based on the downstream task of cause-effect estimation under unobserved confounding. Our approach uses identification via fixing and a symbolic verifier to quantify how graph differences distort cause-effect estimands for different treatment-outcome pairs. We analyze the behavior of the measure under different graph perturbations and compare it against existing distance metrics.

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References · 18

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  3. 2019Causality matters in medical imagingcited by 193
  4. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  5. 2017Causal Structure Learningcited by 84
  6. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  7. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  8. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  9. 2016The State of Applied Econometrics - Causality and Policy Evaluationcited by 41
  10. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  11. 2019The Case for Evaluating Causal Models Using Interventional Measures and Empirical Datacited by 30
  12. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
6 more
  1. 2024Separation-based distance measures for causal graphscited by 9
  2. 2014A factorization criterion for acyclic directed mixed graphscited by 8
  3. 2020Evaluation of Causal Structure Learning Algorithms via Risk Estimationcited by 8
  4. 2023Ananke: A Python Package For Causal Inference Using Graphical Modelscited by 8
  5. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 8
  6. 2020A Ladder of Causal Distancescited by 7

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