{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T05:40:37Z","timestamp":1768801237187,"version":"3.49.0"},"reference-count":62,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000915","name":"Richard King Mellon Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000915","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Economics"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) has an extensive literature documenting positive effects on infant health outcomes, specifically preterm birth, low birthweight, small size for gestational age, and infant mortality. However, existing studies focus on average effects for these relatively infrequent outcomes, thus providing no evidence for how WIC affects those at greatest risk of negative infant health outcomes. Our study focuses on documenting how WIC's infant health effects vary by level of risk. In doing so, we leverage a uniquely rich database describing maternal and infant outcomes and risk factors. Additionally, we use high dimensional data to generate predictions of risk and combine these predictions with the novel double machine learning method to stratify the effects of WIC by predicted risk. Our estimates of WIC's average treatment effects align with those in the existing literature. More importantly, we document significant variation in the effects of WIC on infant health by predicted risk level. Our results show that WIC is most beneficial among those at greatest risk of poor outcomes.<\/jats:p>","DOI":"10.1002\/hec.4617","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T15:38:55Z","timestamp":1666021135000},"page":"194-217","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Variation in the infant health effects of the women, infants, and children program by predicted risk using novel machine learning methods"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6147-3475","authenticated-orcid":false,"given":"Evan D.","family":"Peet","sequence":"first","affiliation":[{"name":"RAND Corporation  Pittsburgh Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dana","family":"Schultz","sequence":"additional","affiliation":[{"name":"RAND Corporation  Pittsburgh Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susan","family":"Lovejoy","sequence":"additional","affiliation":[{"name":"RAND Corporation  Pittsburgh Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuchiang (Rich)","family":"Tsui","sequence":"additional","affiliation":[{"name":"Perelman School of Medicine University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010economics\u2010080217\u2010053402"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/rest_a_00732"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1251872"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1257\/jep.31.2.3"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.epirev.a036128"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","unstructured":"Bertrand M. Cr\u00e9pon B. Marguerie A. &Premand P.(2017).Contemporaneous and post\u2010program impacts of a public works program.","DOI":"10.1596\/28460"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/pam.20070"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/10824669.2018.1523734"},{"key":"e_1_2_12_10_1","volume-title":"Prenatal WIC participation and infant health: Selection and maternal fixed effects","author":"Brien M. 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K\u00f6gel H.&Spindler M.(2019).Heterogeneous effects of poverty on cognition.","DOI":"10.2139\/ssrn.3505970"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpubeco.2008.08.003"},{"key":"e_1_2_12_21_1","unstructured":"Foster D. J. &Syrgkanis V.(2019).Orthogonal statistical learning. arXiv preprint arXiv:1901.09036."},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475\u20106773.2010.01115.x"},{"key":"e_1_2_12_23_1","unstructured":"FRAC.(2019). \u201cMaking WIC work better: Strategies to reach more women and children and strengthen benefits use.\u201d."},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0167\u20109473(01)00065\u20102"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.2307\/1907330"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq439"},{"key":"e_1_2_12_27_1","unstructured":"Global W.(2019).Reinvigorating and reimagining the Pennsylvania WIC program."},{"key":"e_1_2_12_28_1","volume-title":"Understanding the sources of earnings losses after job displacement: A machine\u2010learning approach","author":"Gulyas A.","year":"2019"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2019.1706"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.3386\/w15589"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1257\/app.1.4.109"},{"key":"e_1_2_12_32_1","volume-title":"National and state\u2010level estimates of special supplemental nutrition program for women, infants, and children (WIC) eligibles and program reach, 2011","author":"Johnson P.","year":"2015"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/pam.20131"},{"key":"e_1_2_12_34_1","doi-asserted-by":"publisher","DOI":"10.1002\/pam.20325"},{"key":"e_1_2_12_35_1","doi-asserted-by":"publisher","DOI":"10.2105\/ajph.2009.168922"},{"key":"e_1_2_12_36_1","unstructured":"Knaus M. 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D.(2020). \"Examining interventions to address infant mortality in Allegheny county.\" RAND ReportRR\u2010A858\u20101."},{"key":"e_1_2_12_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10995\u2010016\u20101951\u2010y"},{"key":"e_1_2_12_54_1","unstructured":"Strittmatter A.(2018).What is the value added by using causal machine learning methods in a welfare experiment evaluation?arXiv preprint arXiv:1812.06533."},{"key":"e_1_2_12_55_1","doi-asserted-by":"publisher","DOI":"10.2202\/1935\u20101682.2352"},{"key":"e_1_2_12_56_1","unstructured":"Tsui F. R. &Shi L.(2019).Predicting infant mortality from delivery electronic health records. InAMIA clinical informatics conference."},{"key":"e_1_2_12_57_1","volume-title":"Comprehensive data repository towards infant mortality prediction","author":"Tsui F. 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