mlsanityData to 7 Oct 2026

Results on Counterfactual Invariance

arXiv 2307.08519 ↗First posted 17 Jul 2023cs.LG stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

In this paper we provide a theoretical analysis of counterfactual invariance. We present a variety of existing definitions, study how they relate to each other and what their graphical implications are. We then turn to the current major question surrounding counterfactual invariance, how does it relate to conditional independence? We show that whilst counterfactual invariance implies conditional independence, conditional independence does not give any implications about the degree or likelihood of satisfying counterfactual invariance. Furthermore, we show that for discrete causal models counterfactually invariant functions are often constrained to be functions of particular variables, or even constant.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

10 of 18 references.

References · 10

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  3. 2017Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributionscited by 125
  4. 2018Invariance, Causality and Robustnesscited by 123
  5. 2021Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Testscited by 77
  6. 2013Counterfactual Probabilities: Computational Methods, Bounds and Applicationscited by 67
  7. 2013Strong Completeness and Faithfulness in Bayesian Networkscited by 44
  8. 2022Causal Inference Through the Structural Causal Marginal Problemcited by 22
  9. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  10. 2022Learning Counterfactually Invariant Predictorscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learningcited by 8
  2. 2024Test-Time Fairness and Robustness in Large Language Modelscited by 3
  3. 2025AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysiscited by 1
  4. 2026MELD: Multi-Task Equilibrated Learning Detector for AI-Generated Textcited by 1