Teh, Kai;
Sadeghi, kayvan;
Soo, Terry;
(2025)
Towards Robust Causal Effect Identification beyond Markov Equivalence.
In:
Proceedings of the 42nd International Conference on Machine Learning.
(pp. pp. 1-8).
PMLR
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Abstract
Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold.
| Type: | Proceedings paper |
|---|---|
| Title: | Towards Robust Causal Effect Identification beyond Markov Equivalence |
| Event: | The 42nd International Conference on Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://openreview.net/forum?id=9ahwbMbMM1 |
| Language: | English |
| Additional information: | © The Authors 2025. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Causal Effect Identification, Graphical Models, Robustness, Causal Inference |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10213280 |
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