Propensity Score-Matching Methods for Nonexperimental Causal Studies
This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units...
Gespeichert in:
| Veröffentlicht in: | The review of economics and statistics 2002-02, Vol.84 (1), p.151-161 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 161 |
|---|---|
| container_issue | 1 |
| container_start_page | 151 |
| container_title | The review of economics and statistics |
| container_volume | 84 |
| creator | Dehejia, Rajeev H. Wahba, Sadek |
| description | This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units. |
| doi_str_mv | 10.1162/003465302317331982 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_journals_194678481</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>3211745</jstor_id><sourcerecordid>3211745</sourcerecordid><originalsourceid>FETCH-LOGICAL-c567t-c13716579c5f13e7497a3082543c78e5fe21c93ef24fe6650ced10afeb44b7763</originalsourceid><addsrcrecordid>eNp9kF9LwzAUxYMoOKdfQHwovnfm5k_TPsp0KmwqTJ9Dl926jq2pSSrOT2_GRATFpwOXc37ncgg5BToAyNgFpVxkklPGQXEORc72SA_iIS1AsH3S2xrS6JCH5Mj7JaUUFPAeuXp0tsXG12GTTI11mE7KYBZ185JMMCzs3CeVdcm9bfC9RVevsQnlKhmWnY8yDd28Rn9MDqpy5fHkS_vkeXT9NLxNxw83d8PLcWpkpkJqgCvIpCqMrICjEoUqOc2ZFNyoHGWFDEzBsWKiwiyT1OAcaFnhTIiZUhnvk_Mdt3X2tUMf9NJ2romVGgqRqVzkEE1sZzLOeu-w0m18u3QbDVRvx9K_x4qh0S60rn9AXax4y0UNmlMBnGlGGUSGpoX-qNu_QYM_QP82n-0CSx-s-_6VMwAlJP8Ewn-Gag</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>194678481</pqid></control><display><type>article</type><title>Propensity Score-Matching Methods for Nonexperimental Causal Studies</title><source>Business Source Complete</source><source>JSTOR Mathematics & Statistics</source><source>Jstor Complete Legacy</source><source>MIT Press Journals</source><creator>Dehejia, Rajeev H. ; Wahba, Sadek</creator><creatorcontrib>Dehejia, Rajeev H. ; Wahba, Sadek</creatorcontrib><description>This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.</description><identifier>ISSN: 0034-6535</identifier><identifier>EISSN: 1530-9142</identifier><identifier>DOI: 10.1162/003465302317331982</identifier><identifier>CODEN: RECSA9</identifier><language>eng</language><publisher>238 Main St., Suite 500, Cambridge, MA 02142-1046, USA: MIT Press</publisher><subject>Bias ; Calipers ; Comparative analysis ; Control groups ; Economic models ; Economic theory ; Estimating techniques ; Estimation bias ; Estimation methods ; Estimators ; Human resources ; Observational research ; Observational studies ; Pretreatment ; Standard error ; Studies ; Variables</subject><ispartof>The review of economics and statistics, 2002-02, Vol.84 (1), p.151-161</ispartof><rights>Copyright 2002 The President and Fellows of Harvard College and the Massachusetts Institute of Technology</rights><rights>Copyright MIT Press Journals Feb 2002</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c567t-c13716579c5f13e7497a3082543c78e5fe21c93ef24fe6650ced10afeb44b7763</citedby><cites>FETCH-LOGICAL-c567t-c13716579c5f13e7497a3082543c78e5fe21c93ef24fe6650ced10afeb44b7763</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/3211745$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/3211745$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,828,27903,27904,53987,53988,57995,57999,58228,58232</link.rule.ids></links><search><creatorcontrib>Dehejia, Rajeev H.</creatorcontrib><creatorcontrib>Wahba, Sadek</creatorcontrib><title>Propensity Score-Matching Methods for Nonexperimental Causal Studies</title><title>The review of economics and statistics</title><description>This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.</description><subject>Bias</subject><subject>Calipers</subject><subject>Comparative analysis</subject><subject>Control groups</subject><subject>Economic models</subject><subject>Economic theory</subject><subject>Estimating techniques</subject><subject>Estimation bias</subject><subject>Estimation methods</subject><subject>Estimators</subject><subject>Human resources</subject><subject>Observational research</subject><subject>Observational studies</subject><subject>Pretreatment</subject><subject>Standard error</subject><subject>Studies</subject><subject>Variables</subject><issn>0034-6535</issn><issn>1530-9142</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><recordid>eNp9kF9LwzAUxYMoOKdfQHwovnfm5k_TPsp0KmwqTJ9Dl926jq2pSSrOT2_GRATFpwOXc37ncgg5BToAyNgFpVxkklPGQXEORc72SA_iIS1AsH3S2xrS6JCH5Mj7JaUUFPAeuXp0tsXG12GTTI11mE7KYBZ185JMMCzs3CeVdcm9bfC9RVevsQnlKhmWnY8yDd28Rn9MDqpy5fHkS_vkeXT9NLxNxw83d8PLcWpkpkJqgCvIpCqMrICjEoUqOc2ZFNyoHGWFDEzBsWKiwiyT1OAcaFnhTIiZUhnvk_Mdt3X2tUMf9NJ2romVGgqRqVzkEE1sZzLOeu-w0m18u3QbDVRvx9K_x4qh0S60rn9AXax4y0UNmlMBnGlGGUSGpoX-qNu_QYM_QP82n-0CSx-s-_6VMwAlJP8Ewn-Gag</recordid><startdate>20020201</startdate><enddate>20020201</enddate><creator>Dehejia, Rajeev H.</creator><creator>Wahba, Sadek</creator><general>MIT Press</general><general>MIT Press Journals, The</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20020201</creationdate><title>Propensity Score-Matching Methods for Nonexperimental Causal Studies</title><author>Dehejia, Rajeev H. ; Wahba, Sadek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c567t-c13716579c5f13e7497a3082543c78e5fe21c93ef24fe6650ced10afeb44b7763</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Bias</topic><topic>Calipers</topic><topic>Comparative analysis</topic><topic>Control groups</topic><topic>Economic models</topic><topic>Economic theory</topic><topic>Estimating techniques</topic><topic>Estimation bias</topic><topic>Estimation methods</topic><topic>Estimators</topic><topic>Human resources</topic><topic>Observational research</topic><topic>Observational studies</topic><topic>Pretreatment</topic><topic>Standard error</topic><topic>Studies</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dehejia, Rajeev H.</creatorcontrib><creatorcontrib>Wahba, Sadek</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>The review of economics and statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dehejia, Rajeev H.</au><au>Wahba, Sadek</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Propensity Score-Matching Methods for Nonexperimental Causal Studies</atitle><jtitle>The review of economics and statistics</jtitle><date>2002-02-01</date><risdate>2002</risdate><volume>84</volume><issue>1</issue><spage>151</spage><epage>161</epage><pages>151-161</pages><issn>0034-6535</issn><eissn>1530-9142</eissn><coden>RECSA9</coden><abstract>This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pre-treatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.</abstract><cop>238 Main St., Suite 500, Cambridge, MA 02142-1046, USA</cop><pub>MIT Press</pub><doi>10.1162/003465302317331982</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0034-6535 |
| ispartof | The review of economics and statistics, 2002-02, Vol.84 (1), p.151-161 |
| issn | 0034-6535 1530-9142 |
| language | eng |
| recordid | cdi_proquest_journals_194678481 |
| source | Business Source Complete; JSTOR Mathematics & Statistics; Jstor Complete Legacy; MIT Press Journals |
| subjects | Bias Calipers Comparative analysis Control groups Economic models Economic theory Estimating techniques Estimation bias Estimation methods Estimators Human resources Observational research Observational studies Pretreatment Standard error Studies Variables |
| title | Propensity Score-Matching Methods for Nonexperimental Causal Studies |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T07%3A55%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Propensity%20Score-Matching%20Methods%20for%20Nonexperimental%20Causal%20Studies&rft.jtitle=The%20review%20of%20economics%20and%20statistics&rft.au=Dehejia,%20Rajeev%20H.&rft.date=2002-02-01&rft.volume=84&rft.issue=1&rft.spage=151&rft.epage=161&rft.pages=151-161&rft.issn=0034-6535&rft.eissn=1530-9142&rft.coden=RECSA9&rft_id=info:doi/10.1162/003465302317331982&rft_dat=%3Cjstor_proqu%3E3211745%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=194678481&rft_id=info:pmid/&rft_jstor_id=3211745&rfr_iscdi=true |