Simulating Counterfactuals

Juha Karvanen, Santtu Tikka, Matti Vihola

JAIR 2024 pp. 835-857

doi:10.1613/JAIR.1.15579 /jair/2024/karvanen2024jair-simulating/

Abstract

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.

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Text

Karvanen et al. "Simulating Counterfactuals." Journal of Artificial Intelligence Research, 2024. doi:10.1613/JAIR.1.15579

Markdown

[Karvanen et al. "Simulating Counterfactuals." Journal of Artificial Intelligence Research, 2024.](https://mlanthology.org/jair/2024/karvanen2024jair-simulating/) doi:10.1613/JAIR.1.15579

BibTeX

@article{karvanen2024jair-simulating,
  title     = {{Simulating Counterfactuals}},
  author    = {Karvanen, Juha and Tikka, Santtu and Vihola, Matti},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2024},
  pages     = {835-857},
  doi       = {10.1613/JAIR.1.15579},
  volume    = {80},
  url       = {https://mlanthology.org/jair/2024/karvanen2024jair-simulating/}
}