High-dimensional regression adjustments in randomized experiments
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wager, Stefan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Proceedings of the National Academy of Sciences of the United States of America - Washington, DC : NAS, 1877, 113(2016), 45, Seite 12673-12678 |
|---|---|
| Übergeordnetes Werk: |
volume:113 ; year:2016 ; number:45 ; pages:12673-12678 |
| Links: |
|---|
| DOI / URN: |
10.1073/pnas.1614732113 |
|---|
| Katalog-ID: |
OLC1986421252 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1986421252 | ||
| 003 | DE-627 | ||
| 005 | 20230714234811.0 | ||
| 007 | tu | ||
| 008 | 161202s2016 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1073/pnas.1614732113 |2 doi | |
| 028 | 5 | 2 | |a PQ20161201 |
| 035 | |a (DE-627)OLC1986421252 | ||
| 035 | |a (DE-599)GBVOLC1986421252 | ||
| 035 | |a (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 | ||
| 035 | |a (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 500 |q DNB |
| 082 | 0 | 4 | |a 570 |q AVZ |
| 084 | |a LING |2 fid | ||
| 084 | |a BIODIV |2 fid | ||
| 100 | 1 | |a Wager, Stefan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a High-dimensional regression adjustments in randomized experiments |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. | ||
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Estimating techniques | |
| 650 | 4 | |a Neural networks | |
| 700 | 1 | |a Du, Wenfei |4 oth | |
| 700 | 1 | |a Taylor, Jonathan |4 oth | |
| 700 | 1 | |a Tibshirani, Robert J |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Proceedings of the National Academy of Sciences of the United States of America |d Washington, DC : NAS, 1877 |g 113(2016), 45, Seite 12673-12678 |w (DE-627)129505269 |w (DE-600)209104-5 |w (DE-576)014909189 |x 0027-8424 |7 nnns |
| 773 | 1 | 8 | |g volume:113 |g year:2016 |g number:45 |g pages:12673-12678 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1073/pnas.1614732113 |3 Volltext |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1840668659 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a FID-LING | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a SSG-OLC-CHE | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-FOR | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OLC-DE-84 | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a SSG-OPC-FOR | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_59 | ||
| 951 | |a AR | ||
| 952 | |d 113 |j 2016 |e 45 |h 12673-12678 | ||
| author_variant |
s w sw |
|---|---|
| matchkey_str |
article:00278424:2016----::ihiesoargesoajsmnsnad |
| hierarchy_sort_str |
2016 |
| publishDate |
2016 |
| allfields |
10.1073/pnas.1614732113 doi PQ20161201 (DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Wager, Stefan verfasserin aut High-dimensional regression adjustments in randomized experiments 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. Regression analysis Artificial intelligence Estimating techniques Neural networks Du, Wenfei oth Taylor, Jonathan oth Tibshirani, Robert J oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 45, Seite 12673-12678 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:45 pages:12673-12678 http://dx.doi.org/10.1073/pnas.1614732113 Volltext http://search.proquest.com/docview/1840668659 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 45 12673-12678 |
| spelling |
10.1073/pnas.1614732113 doi PQ20161201 (DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Wager, Stefan verfasserin aut High-dimensional regression adjustments in randomized experiments 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. Regression analysis Artificial intelligence Estimating techniques Neural networks Du, Wenfei oth Taylor, Jonathan oth Tibshirani, Robert J oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 45, Seite 12673-12678 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:45 pages:12673-12678 http://dx.doi.org/10.1073/pnas.1614732113 Volltext http://search.proquest.com/docview/1840668659 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 45 12673-12678 |
| allfields_unstemmed |
10.1073/pnas.1614732113 doi PQ20161201 (DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Wager, Stefan verfasserin aut High-dimensional regression adjustments in randomized experiments 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. Regression analysis Artificial intelligence Estimating techniques Neural networks Du, Wenfei oth Taylor, Jonathan oth Tibshirani, Robert J oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 45, Seite 12673-12678 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:45 pages:12673-12678 http://dx.doi.org/10.1073/pnas.1614732113 Volltext http://search.proquest.com/docview/1840668659 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 45 12673-12678 |
| allfieldsGer |
10.1073/pnas.1614732113 doi PQ20161201 (DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Wager, Stefan verfasserin aut High-dimensional regression adjustments in randomized experiments 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. Regression analysis Artificial intelligence Estimating techniques Neural networks Du, Wenfei oth Taylor, Jonathan oth Tibshirani, Robert J oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 45, Seite 12673-12678 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:45 pages:12673-12678 http://dx.doi.org/10.1073/pnas.1614732113 Volltext http://search.proquest.com/docview/1840668659 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 45 12673-12678 |
| allfieldsSound |
10.1073/pnas.1614732113 doi PQ20161201 (DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex DE-627 ger DE-627 rakwb eng 500 DNB 570 AVZ LING fid BIODIV fid Wager, Stefan verfasserin aut High-dimensional regression adjustments in randomized experiments 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. Regression analysis Artificial intelligence Estimating techniques Neural networks Du, Wenfei oth Taylor, Jonathan oth Tibshirani, Robert J oth Enthalten in Proceedings of the National Academy of Sciences of the United States of America Washington, DC : NAS, 1877 113(2016), 45, Seite 12673-12678 (DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 0027-8424 nnns volume:113 year:2016 number:45 pages:12673-12678 http://dx.doi.org/10.1073/pnas.1614732113 Volltext http://search.proquest.com/docview/1840668659 GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 AR 113 2016 45 12673-12678 |
| language |
English |
| source |
Enthalten in Proceedings of the National Academy of Sciences of the United States of America 113(2016), 45, Seite 12673-12678 volume:113 year:2016 number:45 pages:12673-12678 |
| sourceStr |
Enthalten in Proceedings of the National Academy of Sciences of the United States of America 113(2016), 45, Seite 12673-12678 volume:113 year:2016 number:45 pages:12673-12678 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Regression analysis Artificial intelligence Estimating techniques Neural networks |
| dewey-raw |
500 |
| isfreeaccess_bool |
false |
| container_title |
Proceedings of the National Academy of Sciences of the United States of America |
| authorswithroles_txt_mv |
Wager, Stefan @@aut@@ Du, Wenfei @@oth@@ Taylor, Jonathan @@oth@@ Tibshirani, Robert J @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
129505269 |
| dewey-sort |
3500 |
| id |
OLC1986421252 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1986421252</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714234811.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161202s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1073/pnas.1614732113</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161201</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1986421252</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1986421252</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex</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">500</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wager, Stefan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">High-dimensional regression adjustments in randomized experiments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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="520" ind1=" " ind2=" "><subfield code="a">We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimating techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Wenfei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taylor, Jonathan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tibshirani, Robert J</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Proceedings of the National Academy of Sciences of the United States of America</subfield><subfield code="d">Washington, DC : NAS, 1877</subfield><subfield code="g">113(2016), 45, Seite 12673-12678</subfield><subfield code="w">(DE-627)129505269</subfield><subfield code="w">(DE-600)209104-5</subfield><subfield code="w">(DE-576)014909189</subfield><subfield code="x">0027-8424</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:113</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:45</subfield><subfield code="g">pages:12673-12678</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1073/pnas.1614732113</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1840668659</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">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_59</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">113</subfield><subfield code="j">2016</subfield><subfield code="e">45</subfield><subfield code="h">12673-12678</subfield></datafield></record></collection>
|
| author |
Wager, Stefan |
| spellingShingle |
Wager, Stefan ddc 500 ddc 570 fid LING fid BIODIV misc Regression analysis misc Artificial intelligence misc Estimating techniques misc Neural networks High-dimensional regression adjustments in randomized experiments |
| authorStr |
Wager, Stefan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129505269 |
| format |
Article |
| dewey-ones |
500 - Natural sciences & mathematics 570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0027-8424 |
| topic_title |
500 DNB 570 AVZ LING fid BIODIV fid High-dimensional regression adjustments in randomized experiments Regression analysis Artificial intelligence Estimating techniques Neural networks |
| topic |
ddc 500 ddc 570 fid LING fid BIODIV misc Regression analysis misc Artificial intelligence misc Estimating techniques misc Neural networks |
| topic_unstemmed |
ddc 500 ddc 570 fid LING fid BIODIV misc Regression analysis misc Artificial intelligence misc Estimating techniques misc Neural networks |
| topic_browse |
ddc 500 ddc 570 fid LING fid BIODIV misc Regression analysis misc Artificial intelligence misc Estimating techniques misc Neural networks |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
w d wd j t jt r j t rj rjt |
| hierarchy_parent_title |
Proceedings of the National Academy of Sciences of the United States of America |
| hierarchy_parent_id |
129505269 |
| dewey-tens |
500 - Science 570 - Life sciences; biology |
| hierarchy_top_title |
Proceedings of the National Academy of Sciences of the United States of America |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129505269 (DE-600)209104-5 (DE-576)014909189 |
| title |
High-dimensional regression adjustments in randomized experiments |
| ctrlnum |
(DE-627)OLC1986421252 (DE-599)GBVOLC1986421252 (PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0 (KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex |
| title_full |
High-dimensional regression adjustments in randomized experiments |
| author_sort |
Wager, Stefan |
| journal |
Proceedings of the National Academy of Sciences of the United States of America |
| journalStr |
Proceedings of the National Academy of Sciences of the United States of America |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
12673 |
| author_browse |
Wager, Stefan |
| container_volume |
113 |
| class |
500 DNB 570 AVZ LING fid BIODIV fid |
| format_se |
Aufsätze |
| author-letter |
Wager, Stefan |
| doi_str_mv |
10.1073/pnas.1614732113 |
| dewey-full |
500 570 |
| title_sort |
high-dimensional regression adjustments in randomized experiments |
| title_auth |
High-dimensional regression adjustments in randomized experiments |
| abstract |
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. |
| abstractGer |
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. |
| abstract_unstemmed |
We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-LING FID-BIODIV SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-FOR SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-MAT SSG-OPC-FOR GBV_ILN_40 GBV_ILN_59 |
| container_issue |
45 |
| title_short |
High-dimensional regression adjustments in randomized experiments |
| url |
http://dx.doi.org/10.1073/pnas.1614732113 http://search.proquest.com/docview/1840668659 |
| remote_bool |
false |
| author2 |
Du, Wenfei Taylor, Jonathan Tibshirani, Robert J |
| author2Str |
Du, Wenfei Taylor, Jonathan Tibshirani, Robert J |
| ppnlink |
129505269 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1073/pnas.1614732113 |
| up_date |
2024-07-04T04:38:02.470Z |
| _version_ |
1803621899929911296 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1986421252</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714234811.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161202s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1073/pnas.1614732113</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161201</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1986421252</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1986421252</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c689-69f00b8f8a5bf7329ea1e6cbaab29474290eb40be45fc26d77e5535701b655e0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0583363920160000113004512673highdimensionalregressionadjustmentsinrandomizedex</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">500</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">LING</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="2">fid</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wager, Stefan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">High-dimensional regression adjustments in randomized experiments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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="520" ind1=" " ind2=" "><subfield code="a">We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation and flexible nonparametric regression adjustments with machine-learning methods such as random forests or neural networks.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Estimating techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural networks</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Wenfei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taylor, Jonathan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tibshirani, Robert J</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Proceedings of the National Academy of Sciences of the United States of America</subfield><subfield code="d">Washington, DC : NAS, 1877</subfield><subfield code="g">113(2016), 45, Seite 12673-12678</subfield><subfield code="w">(DE-627)129505269</subfield><subfield code="w">(DE-600)209104-5</subfield><subfield code="w">(DE-576)014909189</subfield><subfield code="x">0027-8424</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:113</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:45</subfield><subfield code="g">pages:12673-12678</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1073/pnas.1614732113</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1840668659</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">FID-LING</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_59</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">113</subfield><subfield code="j">2016</subfield><subfield code="e">45</subfield><subfield code="h">12673-12678</subfield></datafield></record></collection>
|
| score |
7.3996515 |