{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T07:59:11Z","timestamp":1780905551862,"version":"3.54.1"},"reference-count":27,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The rise of machine learning (ML) is one of the most prominent developments in applied econometrics in the past decade. The focus of much economic analysis is causal, rather than prediction, and Belloni et al. (2014) demonstrate how ML methods can be used in causal inference. This paper undertakes a narrow and wide replication of the Monte Carlo and empirical examples presented by Belloni et al. (2014). We discuss practical implications of this replication for the use of double ML methods in applied econometric research.<\/jats:p>","DOI":"10.1002\/jae.70041","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T03:14:47Z","timestamp":1771211687000},"page":"499-506","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Double LASSO: Replication and Practical Insights"],"prefix":"10.1002","volume":"41","author":[{"given":"Jack","family":"Fitzgerald Sice","sequence":"first","affiliation":[{"name":"Gradient Institute  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Finn","family":"Lattimore","sequence":"additional","affiliation":[{"name":"Economic Research Department Reserve Bank of Australia  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Robinson","sequence":"additional","affiliation":[{"name":"Melbourne Institute of Applied Economic and Social Research University of Melbourne  Parkville Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Economics, Finance and Marketing RMIT University  Melbourne Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1086\/717933"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1515\/9781400829828"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Bach P. V.Chernozhukov M. S.Kurz andM.Spindler.2021.\u201cDoubleML\u00a0\u2013\u00a0An Object\u2010Oriented Implementation of Double Machine Learning in R \u201dstat.ML arXiv:2103.09603 arXiv.","DOI":"10.32614\/CRAN.package.DoubleML"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/ectj\/utae004"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA9626"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/restud\/rdt044"},{"issue":"12","key":"e_1_2_10_8_1","first-page":"102","article-title":"Do Instrumental Variables Belong in Propensity Scores?","volume":"9","author":"Bhattacharya J.","year":"2012","journal-title":"International Journal of Statistics and Economics"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/ectj.12097"},{"key":"e_1_2_10_10_1","doi-asserted-by":"crossref","unstructured":"Chernozhukov V. 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