English

Results on Counterfactual Invariance

Machine Learning 2023-07-18 v1 Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2307.08519,
  title  = {Results on Counterfactual Invariance},
  author = {Jake Fawkes and Robin J. Evans},
  journal= {arXiv preprint arXiv:2307.08519},
  year   = {2023}
}

Comments

5 pages with 6 pages of supplementary. Accepted at the ICML 2023 workshop on Spurious Correlations, Invariance and Stability

R2 v1 2026-06-28T11:32:32.117Z