CausalCERTIFAI: A Causality-Aware Framework for Counterfactual Explanations

Cankut Yaman, Süreyya Akyüz
Proceedings of Fifth European Conference on Algorithmic Fairness, PMLR 350:136-147, 2026.

Abstract

This study introduces CausalCERTIFAI, a causality-aware framework for generating counterfactual explanations in tabular machine learning models. The proposed approach integrates graph-based causal discovery with a constraint-aware genetic search process to produce counterfactuals that achieve a desired prediction while remaining consistent with learned causal relationships. Experiments are conducted on multiple classification datasets using decision trees, XGBoost, and LightGBM, comparing standard counterfactual generation with a causal constrained variant. Performance is evaluated in terms of robustness using NCERScore, alongside computational efficiency measured by runtime per instance. Results show that introducing causal constraints increases normalised counterfactual distance, reflecting more restricted but more realistic recourse, with stronger effects observed in higher-dimensional datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v350-yaman26a, title = {CausalCERTIFAI: A Causality-Aware Framework for Counterfactual Explanations}, author = {Yaman, Cankut and Aky\"uz, S\"ureyya}, booktitle = {Proceedings of Fifth European Conference on Algorithmic Fairness}, pages = {136--147}, year = {2026}, editor = {De Bie, Tijl and Defrance, MaryBeth and Calders, Toon and Nguyen, Dennis}, volume = {350}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v350/main/assets/yaman26a/yaman26a.pdf}, url = {https://proceedings.mlr.press/v350/yaman26a.html}, abstract = {This study introduces CausalCERTIFAI, a causality-aware framework for generating counterfactual explanations in tabular machine learning models. The proposed approach integrates graph-based causal discovery with a constraint-aware genetic search process to produce counterfactuals that achieve a desired prediction while remaining consistent with learned causal relationships. Experiments are conducted on multiple classification datasets using decision trees, XGBoost, and LightGBM, comparing standard counterfactual generation with a causal constrained variant. Performance is evaluated in terms of robustness using NCERScore, alongside computational efficiency measured by runtime per instance. Results show that introducing causal constraints increases normalised counterfactual distance, reflecting more restricted but more realistic recourse, with stronger effects observed in higher-dimensional datasets.} }
Endnote
%0 Conference Paper %T CausalCERTIFAI: A Causality-Aware Framework for Counterfactual Explanations %A Cankut Yaman %A Süreyya Akyüz %B Proceedings of Fifth European Conference on Algorithmic Fairness %C Proceedings of Machine Learning Research %D 2026 %E Tijl De Bie %E MaryBeth Defrance %E Toon Calders %E Dennis Nguyen %F pmlr-v350-yaman26a %I PMLR %P 136--147 %U https://proceedings.mlr.press/v350/yaman26a.html %V 350 %X This study introduces CausalCERTIFAI, a causality-aware framework for generating counterfactual explanations in tabular machine learning models. The proposed approach integrates graph-based causal discovery with a constraint-aware genetic search process to produce counterfactuals that achieve a desired prediction while remaining consistent with learned causal relationships. Experiments are conducted on multiple classification datasets using decision trees, XGBoost, and LightGBM, comparing standard counterfactual generation with a causal constrained variant. Performance is evaluated in terms of robustness using NCERScore, alongside computational efficiency measured by runtime per instance. Results show that introducing causal constraints increases normalised counterfactual distance, reflecting more restricted but more realistic recourse, with stronger effects observed in higher-dimensional datasets.
APA
Yaman, C. & Akyüz, S.. (2026). CausalCERTIFAI: A Causality-Aware Framework for Counterfactual Explanations. Proceedings of Fifth European Conference on Algorithmic Fairness, in Proceedings of Machine Learning Research 350:136-147 Available from https://proceedings.mlr.press/v350/yaman26a.html.

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