Bounding Counterfactuals Under Selection Bias
Abstract
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the derivation of identifiability conditions. We propose instead a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal. This enables us to use the causal expectation-maximisation scheme to obtain the values of causal queries in the identifiable case, and to compute bounds otherwise. Experiments demonstrate the approach to be practically viable. Theoretical convergence characterisations are provided.
Cite
Text
Zaffalon et al. "Bounding Counterfactuals Under Selection Bias." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.Markdown
[Zaffalon et al. "Bounding Counterfactuals Under Selection Bias." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.](https://mlanthology.org/pgm/2022/zaffalon2022pgm-bounding/)BibTeX
@inproceedings{zaffalon2022pgm-bounding,
title = {{Bounding Counterfactuals Under Selection Bias}},
author = {Zaffalon, Marco and Antonucci, Alessandro and Cabañas, Rafael and Huber, David and Azzimonti, Dario},
booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
year = {2022},
pages = {289-300},
volume = {186},
url = {https://mlanthology.org/pgm/2022/zaffalon2022pgm-bounding/}
}