{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T09:24:27Z","timestamp":1787131467234,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032083234","type":"print"},{"value":"9783032083241","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In medical applications of machine learning, it is essential to interpret model behavior and explore the true association between features and clinical outcomes. Conventional Explainable AI approaches typically focus on \u201cmodel-dependent\u201d global feature importance from trained models, which may not guarantee \u201cdata-driven\u201d causal relationships or medical consistency. To address this, we propose CausalAIME, a new framework that combines approximate inverse model explanations (AIME) with the Peter-Clark algorithm for causal discovery. This integration suppresses multicollinearity while enabling global visualization of both feature signs (positive or negative) and class-specific contributions. Furthermore, by choosing either the model\u2019s output\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\hat{Y}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mover>\n                            <mml:mi>Y<\/mml:mi>\n                            <mml:mo>^<\/mml:mo>\n                          <\/mml:mover>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    or the true label\n                    <jats:italic>Y<\/jats:italic>\n                    as input, CausalAIME unifies both model-dependent and data-driven global feature importance within the same framework. Our experiments using breast cancer diagnostic data compared CausalAIME with existing methods such as Random Forest and SHAP, highlighting the advantages of CausalAIME in offering sign-based interpretability and causal perspectives, both critical in clinical settings. We anticipate that CausalAIME will contribute to enhanced explainability across various domains, including healthcare, by accommodating the needs for both true association analysis and model behavior interpretation.\n                  <\/jats:p>","DOI":"10.1007\/978-3-032-08324-1_15","type":"book-chapter","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T08:49:23Z","timestamp":1760518163000},"page":"332-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["CausalAIME: Leveraging Peter-Clark Algorithms and\u00a0Inverse Modeling for\u00a0Unified Global Feature Explanation in\u00a0Healthcare"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1029-6063","authenticated-orcid":false,"given":"Takafumi","family":"Nakanishi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Lundberg, S. M., Lee, S. I.: A unified approach to interpreting model predictions. In: Proceedigs of the 31st International Conference on Neural Information Processing Systems, pp. 4768\u20134777 (2017). https:\/\/doi.org\/10.5555\/3295222.3295230","DOI":"10.5555\/3295222.3295230"},{"key":"15_CR2","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20141144 (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"15_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Nakanishi, T.: Approximate inverse model explanations (AIME): unveiling local and global insights in machine learning models. IEEE Access 11, 101020\u2013101044 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3314336","DOI":"10.1109\/ACCESS.2023.3314336"},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1177\/08944393910090010","volume":"9","author":"P Spirtes","year":"1991","unstructured":"Spirtes, P., Glymour, C.: An algorithm for fast recovery of sparse causal graphs. Soc. Sci. Comput. Rev. 9(1), 62\u201372 (1991). https:\/\/doi.org\/10.1177\/08944393910090010","journal-title":"Soc. Sci. Comput. Rev."},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Wolberg, W., Mangasarian, O., Street, N., Street, W.: Breast cancer wisconsin (diagnostic) [dataset]. UCI Machine Learning Repository (1993). https:\/\/doi.org\/10.24432\/C5DW2B. Accessed 16 Jan 2024","DOI":"10.24432\/C5DW2B"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Takahashi, D., Shimizu, S., Tanaka, T.: Counterfactual explanations of black-box machine learning models using causal discovery with applications to credit rating. In: 2024 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2024). https:\/\/doi.org\/10.1109\/IJCNN60899.2024.10650130","DOI":"10.1109\/IJCNN60899.2024.10650130"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Tyrovolas, M., Kallimanis, N., Stylios, C.: Advancing explainable AI with causal analysis in large-scale fuzzy cognitive maps. ArXiv, abs\/2405.09190 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.09190","DOI":"10.48550\/arXiv.2405.09190"},{"key":"15_CR9","doi-asserted-by":"publisher","unstructured":"Cinquini, M., Guidotti, R.: CALIME: causality-aware local interpretable model-agnostic explanations. In: Proceedings of Explainable Artificial Intelligence. xAI 2024. Communications in Computer and Information Science, vol. 2155. Springer, Cham, (2024). https:\/\/doi.org\/10.1007\/978-3-031-63800-8_6","DOI":"10.1007\/978-3-031-63800-8_6"},{"key":"15_CR10","unstructured":"Termine, A., Antonucci, A., Facchini, A.: Machine learning explanations by surrogate causal models (MaLESCaMo). In: Proceedings of xAI (Late-breaking Work, Demos, Doctoral Consortium), pp. 59\u201364 (2023)"},{"key":"15_CR11","unstructured":"Zecevic, M., Dhami, D., Rothkopf, C., Kersting, K. XAI establishes a common ground between machine learning and causality. In: Proceedings of xAI (Late-breaking Work, Demos, Doctoral Consortium) (2022)"},{"key":"15_CR12","unstructured":"Wang, J., Wiens, J., Lundberg, S.: Shapley flow: a graph-based approach to interpreting model predictions. In: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR, 130, pp. 721\u2013729 (2021)"},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Breuer, N.O., Sauter, A., Mohammadi, M., Acar, E.: CAGE: causality-aware Shapley value for global explanations. In: Proceedings of Explainable Artificial Intelligence, xAI 2024, Communications in Computer and Information Science, vol. 2155. Springer, Cham5. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-63800-8_8","DOI":"10.1007\/978-3-031-63800-8_8"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Polak, E.: Optimization: Algorithms and Consistent Approximations., Applied Mathematical Sciences, Vol. 124. Springer, New York (1997). https:\/\/doi.org\/10.1007\/978-1-4612-0663-7","DOI":"10.1007\/978-1-4612-0663-7"},{"issue":"151","key":"15_CR15","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1090\/S0025-5718-1980-0572855-7","volume":"35","author":"J Nocedal","year":"1980","unstructured":"Nocedal, J.: Updating quasi-newton matrices with limited storage. Math. Comput. 35(151), 773\u2013782 (1980). https:\/\/doi.org\/10.1090\/S0025-5718-1980-0572855-7","journal-title":"Math. Comput."},{"issue":"3","key":"15_CR16","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22(3), 400\u2013407 (1951). https:\/\/doi.org\/10.1214\/aoms\/1177729586","journal-title":"Ann. Math. Stat."},{"key":"15_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015). https:\/\/arxiv.org\/abs\/1412.6980, last accessed 2024\/1\/16"},{"key":"15_CR18","unstructured":"Tieleman, T., Hinton, G.: Lecture 6.5\u2014RMSProp: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning (2012). https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf, last accessed 2024\/1\/16"},{"key":"15_CR19","first-page":"507","volume":"3","author":"DM Chickering","year":"2002","unstructured":"Chickering, D.M.: Optimal structure identification with greedy search. J. Mach. Learn. Res. 3, 507\u2013554 (2002)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR20","unstructured":"Spirtes, P., Meek, C., Richardson, T.: Causal inference in the presence of latent variables and selection bias. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 499\u2013506 (1995)"},{"key":"15_CR21","first-page":"2003","volume":"7","author":"S Shimizu","year":"2006","unstructured":"Shimizu, S., Hoyer, P.O., Hyv\u00e4rinen, A., Kerminen, A.: A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7, 2003\u20132030 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR22","unstructured":"Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)"},{"key":"15_CR23","unstructured":"SHAP. https:\/\/github.com\/shap\/shap. Accessed 16 Jan 2024"},{"key":"15_CR24","unstructured":"SHAP. https:\/\/pypi.org\/project\/shap\/. Accessed 16 Jan 2024"},{"key":"15_CR25","unstructured":"AIME: Approximate Inverse Model Explanations. https:\/\/github.com\/ntakafumi\/aime\/. Accessed 16 Jan 2024"},{"key":"15_CR26","unstructured":"AIME: Approximate Inverse Model Explanations. https:\/\/pypi.org\/project\/aime-xai\/. Accessed 16 Jan 2024"},{"issue":"5","key":"15_CR27","doi-asserted-by":"publisher","first-page":"249","DOI":"10.3747\/co.19.1043","volume":"19","author":"SA Narod","year":"2012","unstructured":"Narod, S.A.: Tumour size predicts long-term survival among women with lymph node-positive breast cancer. Curr. Oncol. 19(5), 249\u2013253 (2012). https:\/\/doi.org\/10.3747\/co.19.1043","journal-title":"Curr. Oncol."},{"key":"15_CR28","doi-asserted-by":"publisher","unstructured":"Chitalia, R.D., Kontos, D.: Role of texture analysis in breast MRI as a cancer biomarker: a review. J. Magn. Reson. Imaging 49(4), 927\u2013938 (2019). https:\/\/doi.org\/10.1002\/jmri.26556","DOI":"10.1002\/jmri.26556"},{"issue":"1","key":"15_CR29","doi-asserted-by":"publisher","first-page":"59","DOI":"10.3390\/biophysica2010005","volume":"2","author":"L Elkington","year":"2022","unstructured":"Elkington, L., Adhikari, P., Pradhan, P.: Fractal dimension analysis to detect the progress of cancer using transmission optical microscopy. Biophysica 2(1), 59\u201369 (2022). https:\/\/doi.org\/10.3390\/biophysica2010005","journal-title":"Biophysica"},{"key":"15_CR30","doi-asserted-by":"publisher","unstructured":"Arreche, O., Guntur, T.R., Roberts, J.W., Abdallah, M.: E-XAI: evaluating black-box explainable AI frameworks for network intrusion detection. In: IEEE Access, vol. 12, pp. 23954\u201423988 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3365140","DOI":"10.1109\/ACCESS.2024.3365140"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08324-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T08:30:12Z","timestamp":1787128212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08324-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"ISBN":["9783032083234","9783032083241"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08324-1_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,16]]},"assertion":[{"value":"16 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article. All aspects of the research, including data collection, analysis, and presentation of the results, were conducted independently and impartially without external influence or financial support that could affect the integrity of the findings.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Istanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcrkiye","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}