mlsanityData to 14 Oct 2026

Simulating counterfactuals

arXiv 2306.15328 ↗First posted 27 Jun 2023stat.ML cs.CY cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit-scoring.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

22 of 58 references.

References · 22

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  4. 2017Counterfactual Fairnesscited by 804
  5. 2020Fairness in Machine Learning: A Surveycited by 370
  6. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  7. 2020A Survey on the Explainability of Supervised Machine Learningcited by 227
  8. 2018Path-Specific Counterfactual Fairnesscited by 181
  9. 2017Fair Inference On Outcomescited by 144
  10. 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
  11. 2019PC-Fairness: A Unified Framework for Measuring Causality-based Fairnesscited by 67
  12. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
10 more
  1. 2023Causal normalizing flows: from theory to practicecited by 51
  2. 2021Nested Counterfactual Identification from Arbitrary Surrogate Experimentscited by 38
  3. 2022Counterfactual harmcited by 29
  4. 2021Clustering and Structural Robustness in Causal Diagramscited by 12
  5. 2013Fighting Sample Degeneracy and Impoverishment in Particle Filters: A Review of Intelligent Approachescited by 7
  6. 2018Enhancing Identification of Causal Effects by Pruningcited by 7
  7. 2022On resampling schemes for particle filters with weakly informative observationscited by 5
  8. 2022Conditional particle filters with bridge backward samplingcited by 4
  9. 2022Identifying Counterfactual Queries with the R Package cfidcited by 2
  10. 2022Convergence of random-weight sequential Monte Carlo methodscited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Clustering and Pruning in Causal Data Fusioncited by 2