Robust Causal Domain Adaptation in a Simple Diagnostic Setting
Abstract
Causal domain adaptation approaches aim to find statistical relations in a source domain, that will still hold in a target domain, using the assumption that a common causal graph underlies both domains. For many such problems, the available information is insufficient to uniquely identify the target domain distribution, and we find a set of distributions instead. We propose to use a worst-case approach, picking an action that performs well against all distributions in this set. In this paper, we study a specific diagnostic instance of this problem, and find a sufficient and necessary condition that characterizes the worst-case distribution in the target domain. We find that the Brier and logarithmic scores lead to different distributions, and consequently to different recommendations for the decision maker.
Cite
Text
Ommen. "Robust Causal Domain Adaptation in a Simple Diagnostic Setting." Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, 2019.Markdown
[Ommen. "Robust Causal Domain Adaptation in a Simple Diagnostic Setting." Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, 2019.](https://mlanthology.org/isipta/2019/ommen2019isipta-robust/)BibTeX
@inproceedings{ommen2019isipta-robust,
title = {{Robust Causal Domain Adaptation in a Simple Diagnostic Setting}},
author = {Ommen, Thijs},
booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications},
year = {2019},
pages = {424-429},
volume = {103},
url = {https://mlanthology.org/isipta/2019/ommen2019isipta-robust/}
}