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We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees.
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC press
1984
Earlier work this paper cites.
Breiman L (2001) Random forests. Machine learning 45(1):5–32
2001
Earlier work this paper cites.
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics pp 1189–1232
2001
Earlier work this paper cites.
Dudík M, Langford J, Li L (2011) Doubly robust policy evaluation and learning. arXiv preprint arXiv:11034601
2011
Earlier work this paper cites.
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 785–794
2016
Earlier work this paper cites.
Athey S, Wager S (2017) Efficient policy learning. arXiv preprint arXiv:170202896
2017
Cited alongside, same era.
Bertsimas D, Dunn J (2017) Optimal classification trees. Machine Learning 106(7):1039–1082
2017
Cited alongside, same era.
Bertsimas D, Kallus N, Weinstein AM, Zhuo YD (2017) Personalized diabetes management using electronic medical records. Diabetes care 40(2):210–217
2017
Cited alongside, same era.
Kallus N (2017) Recursive partitioning for personalization using observational data. In: International Conference on Machine Learning, PMLR, pp 1789–1798
2017
Cited alongside, same era.
Powers S, Qian J, Jung K, Schuler A, Shah NH, Hastie T, Tibshirani R (2018) Some methods for heterogeneous treatment effect estimation in high dimensions. Statistics in medicine 37(11):1767–1787
2018
Cited alongside, same era.
Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association 113(523):1228–1242
2018
Later among the works it cites.
Zhou Z, Athey S, Wager S (2018) Offline multi-action policy learning: Generalization and optimization. arXiv preprint arXiv:181004778
2018
Later among the works it cites.
Bertsimas D, Dunn J (2019) Machine learning under a modern optimization lens. Dynamic Ideas LLC
2019
Later among the works it cites.
Bertsimas D, Dunn J, Mundru N (2019) Optimal prescriptive trees. INFORMS Journal on Optimization 1(2):164–183
2019
Later among the works it cites.
Biggs M, Sun W, Ettl M (2020) Model distillation for revenue optimization: Interpretable personalized pricing. arXiv preprint arXiv:200701903
2020
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