Optimal policy trees
Abstract 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. The resulting method, Optimal Policy Trees,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Amram, Maxime [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Machine learning - Springer US, 1986, 111(2022), 7 vom: 09. März, Seite 2741-2768 |
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| Übergeordnetes Werk: |
volume:111 ; year:2022 ; number:7 ; day:09 ; month:03 ; pages:2741-2768 |
| Links: |
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| DOI / URN: |
10.1007/s10994-022-06128-5 |
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OLC2079084623 |
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| 520 | |a Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. | ||
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| 650 | 4 | |a Prescriptive decision making | |
| 700 | 1 | |a Dunn, Jack |0 (orcid)0000-0002-6936-4502 |4 aut | |
| 700 | 1 | |a Zhuo, Ying Daisy |4 aut | |
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10.1007/s10994-022-06128-5 doi (DE-627)OLC2079084623 (DE-He213)s10994-022-06128-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Amram, Maxime verfasserin aut Optimal policy trees 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. Machine learning Decision trees Prescriptive decision making Dunn, Jack (orcid)0000-0002-6936-4502 aut Zhuo, Ying Daisy aut Enthalten in Machine learning Springer US, 1986 111(2022), 7 vom: 09. März, Seite 2741-2768 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:111 year:2022 number:7 day:09 month:03 pages:2741-2768 https://doi.org/10.1007/s10994-022-06128-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 111 2022 7 09 03 2741-2768 |
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10.1007/s10994-022-06128-5 doi (DE-627)OLC2079084623 (DE-He213)s10994-022-06128-5-p DE-627 ger DE-627 rakwb eng 150 004 VZ Amram, Maxime verfasserin aut Optimal policy trees 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. Machine learning Decision trees Prescriptive decision making Dunn, Jack (orcid)0000-0002-6936-4502 aut Zhuo, Ying Daisy aut Enthalten in Machine learning Springer US, 1986 111(2022), 7 vom: 09. März, Seite 2741-2768 (DE-627)12920403X (DE-600)54638-0 (DE-576)014457377 0885-6125 nnns volume:111 year:2022 number:7 day:09 month:03 pages:2741-2768 https://doi.org/10.1007/s10994-022-06128-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 111 2022 7 09 03 2741-2768 |
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Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 |
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Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 |
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Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems. © The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2079084623</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506034521.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10994-022-06128-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2079084623</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10994-022-06128-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Amram, Maxime</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimal policy trees</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract 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. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. 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März, Seite 2741-2768</subfield><subfield code="w">(DE-627)12920403X</subfield><subfield code="w">(DE-600)54638-0</subfield><subfield code="w">(DE-576)014457377</subfield><subfield code="x">0885-6125</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:111</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:7</subfield><subfield code="g">day:09</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:2741-2768</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10994-022-06128-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">111</subfield><subfield code="j">2022</subfield><subfield code="e">7</subfield><subfield code="b">09</subfield><subfield code="c">03</subfield><subfield code="h">2741-2768</subfield></datafield></record></collection>
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