Simulating counterfactuals
Machine Learning
2024-07-03 v3 Computers and Society
Machine Learning
Computation
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.
Cite
@article{arxiv.2306.15328,
title = {Simulating counterfactuals},
author = {Juha Karvanen and Santtu Tikka and Matti Vihola},
journal= {arXiv preprint arXiv:2306.15328},
year = {2024}
}