{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T23:05:26Z","timestamp":1774479926164,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s10994-022-06128-5","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T18:02:53Z","timestamp":1646848973000},"page":"2741-2768","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Optimal policy trees"],"prefix":"10.1007","volume":"111","author":[{"given":"Maxime","family":"Amram","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-4502","authenticated-orcid":false,"given":"Jack","family":"Dunn","sequence":"additional","affiliation":[]},{"given":"Ying Daisy","family":"Zhuo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"6128_CR1","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.1609\/aaai.v34i04.5711","volume":"34","author":"G Aglin","year":"2020","unstructured":"Aglin, G., Nijssen, S., & Schaus, P. (2020). Learning optimal decision trees using caching branch-and-bound search. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 3146\u20133153.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"6128_CR2","unstructured":"Athey, S., & Wager, S. (2017) Efficient policy learning. arXiv:1702.02896"},{"key":"6128_CR3","unstructured":"Bennett, K.P. (1992). Decision tree construction via linear programming. In Evans, M. (ed.) Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society Conference, pp. 97\u2013101."},{"key":"6128_CR4","unstructured":"Bertsimas, D., & Dunn, J. (2019). Machine learning under a modern optimization lens. Dynamic Ideas LLC."},{"issue":"7","key":"6128_CR5","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1007\/s10994-017-5633-9","volume":"106","author":"D Bertsimas","year":"2017","unstructured":"Bertsimas, D., & Dunn, J. (2017). Optimal classification trees. Machine Learning, 106(7), 1039\u20131082.","journal-title":"Machine Learning"},{"issue":"2","key":"6128_CR6","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1287\/ijoo.2018.0005","volume":"1","author":"D Bertsimas","year":"2019","unstructured":"Bertsimas, D., Dunn, J., & Mundru, N. (2019). Optimal prescriptive trees. INFORMS Journal on Optimization, 1(2), 164\u2013183.","journal-title":"INFORMS Journal on Optimization"},{"issue":"2","key":"6128_CR7","doi-asserted-by":"publisher","first-page":"210","DOI":"10.2337\/dc16-0826","volume":"40","author":"D Bertsimas","year":"2017","unstructured":"Bertsimas, D., Kallus, N., Weinstein, A. M., & Zhuo, Y. D. (2017). Personalized diabetes management using electronic medical records. Diabetes Care, 40(2), 210\u2013217.","journal-title":"Diabetes Care"},{"key":"6128_CR8","unstructured":"Biggs, M., Sun, W., & Ettl, M. (2020). Model distillation for revenue optimization: Interpretable personalized pricing. arXiv:2007.01903."},{"issue":"1","key":"6128_CR9","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332.","journal-title":"Machine Learning"},{"key":"6128_CR10","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press."},{"issue":"1","key":"6128_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s11750-021-00594-1","volume":"29","author":"E Carrizosa","year":"2021","unstructured":"Carrizosa, E., Molero-R\u00edo, C., & Morales, D. R. (2021). Mathematical optimization in classification and regression trees. Top, 29(1), 5\u201333.","journal-title":"Top"},{"key":"6128_CR12","doi-asserted-by":"crossref","unstructured":"Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"6128_CR13","unstructured":"Dud\u00edk, M., Langford, J., & Li, L. (2011). Doubly robust policy evaluation and learning. arXiv:1103.4601."},{"key":"6128_CR14","doi-asserted-by":"crossref","unstructured":"Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics pp. 1189\u20131232.","DOI":"10.1214\/aos\/1013203451"},{"key":"6128_CR15","unstructured":"Kallus, N. (2017). Recursive partitioning for personalization using observational data. In International Conference on Machine Learning, PMLR, pp. 1789\u20131798."},{"issue":"1","key":"6128_CR16","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s10618-010-0174-x","volume":"21","author":"S Nijssen","year":"2010","unstructured":"Nijssen, S., & Fromont, E. (2010). Optimal constraint-based decision tree induction from itemset lattices. Data Mining and Knowledge Discovery, 21(1), 9\u201351.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"11","key":"6128_CR17","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1002\/sim.7623","volume":"37","author":"S Powers","year":"2018","unstructured":"Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in Medicine, 37(11), 1767\u20131787.","journal-title":"Statistics in Medicine"},{"issue":"3","key":"6128_CR18","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/s10601-020-09312-3","volume":"25","author":"H Verhaeghe","year":"2020","unstructured":"Verhaeghe, H., Nijssen, S., Pesant, G., Quimper, C. G., & Schaus, P. (2020). Learning optimal decision trees using constraint programming. Constraints, 25(3), 226\u2013250.","journal-title":"Constraints"},{"key":"6128_CR19","doi-asserted-by":"crossref","unstructured":"Verwer, S., & Zhang, Y. (2017). Learning decision trees with flexible constraints and objectives using integer optimization. In International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Springer, pp. 94\u2013103.","DOI":"10.1007\/978-3-319-59776-8_8"},{"issue":"523","key":"6128_CR20","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","volume":"113","author":"S Wager","year":"2018","unstructured":"Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228\u20131242.","journal-title":"Journal of the American Statistical Association"},{"key":"6128_CR21","unstructured":"Zhou, Z., Athey, S., & Wager, S. (2018). Offline multi-action policy learning: Generalization and optimization. arXiv:1810.04778."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06128-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06128-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06128-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T01:03:39Z","timestamp":1678323819000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06128-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":21,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["6128"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06128-5","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]},"assertion":[{"value":"5 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have financial interests in Interpretable AI LLC.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}