{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:46:32Z","timestamp":1785779192901,"version":"3.56.0"},"reference-count":57,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"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":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Double\/debiased machine learning (DML) uses for estimating an average treatment effect (ATE) a double\u2010robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment given covariates. Estimators relying on double\u2010robust score functions are highly sensitive to errors in propensity score predictions. Machine learning algorithms have been found to produce models that often overestimate or underestimate these probabilities. Several calibration approaches have been proposed to improve probabilistic forecasts of machine learners. This paper explores their integration into the DML framework, showing via simulations that using calibrated propensity scores significantly reduces the root mean squared error of ATE estimates in finite samples while preserving DML's asymptotic properties.<\/jats:p>","DOI":"10.1002\/jae.70057","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T13:13:04Z","timestamp":1778245984000},"page":"613-626","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Finite Sample Estimation of Average Treatment Effects Using Double\/Debiased Machine Learning With Propensity Score Calibration"],"prefix":"10.1002","volume":"41","author":[{"given":"Daniele","family":"Ballinari","sequence":"first","affiliation":[{"name":"Swiss National Bank  Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nora","family":"Bearth","sequence":"additional","affiliation":[{"name":"University of St. Gallen  St. Gallen Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.3103"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2024.111838"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1972.10481216"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-60566-766-9.ch006"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.3150\/11-BEJ410"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/REST_a_00431"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143865"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/ectj.12097"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA18515"},{"key":"e_1_2_11_12_1","first-page":"443","article-title":"Expectation Consistency for Calibration of Neural Networks","volume":"216","author":"Clart\u00e9 L.","year":"2023","journal-title":"Proceedings of the Thirty\u2010Ninth Conference on Uncertainty in Artificial Intelligence"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asn055"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2015.33"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2019.10.014"},{"key":"e_1_2_11_16_1","volume-title":"The 40th Conference on Uncertainty in Artificial Intelligence","author":"Deshpande S.","year":"2024"},{"issue":"2","key":"e_1_2_11_17_1","first-page":"353","article-title":"Machine Learning in the Estimation of Causal Effects: Targeted Minimum Loss\u2010Based Estimation and Double\/Debiased Machine Learning","volume":"21","author":"D\u00edaz I.","year":"2020","journal-title":"Biostatistics"},{"key":"e_1_2_11_18_1","article-title":"Deep Learning for Individual Heterogeneity: An Automatic Inference Framework","author":"Farrell M. H.","year":"2020","journal-title":"arXiv Preprint arXiv:2010.14694"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA16901"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347963"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465304323023697"},{"key":"e_1_2_11_22_1","first-page":"1321","volume-title":"International Conference on Machine Learning","author":"Guo C.","year":"2017"},{"key":"e_1_2_11_23_1","first-page":"3711","article-title":"Distribution\u2010Free Binary Classification: Prediction Sets, Confidence Intervals and Calibration","volume":"33","author":"Gupta C.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_24_1","first-page":"3942","volume-title":"International Conference on Machine Learning","author":"Gupta C.","year":"2021"},{"key":"e_1_2_11_25_1","article-title":"Propensity Score Models Are Better When Post\u2010Calibrated","author":"Gutman R.","year":"2022","journal-title":"arXiv Preprint arXiv:2211.01221"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.2307\/2998560"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2012.11.006"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1257\/jel.47.1.5"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003216223-10"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/ectj\/utaa014"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.3368\/jhr.57.2.0718-9615R1"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-EJS1338SI"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0297.2007.02111.x"},{"key":"e_1_2_11_35_1","article-title":"Swiss Active Labor Market Policy Evaluation [Dataset]","author":"Lechner M.","year":"2020","journal-title":"Distributed by FORS, Lausanne"},{"key":"e_1_2_11_36_1","article-title":"Comprehensive Causal Machine Learning","author":"Lechner M.","year":"2024","journal-title":"arXiv Preprint arXiv:2405.10198"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.labeco.2013.01.004"},{"key":"e_1_2_11_38_1","article-title":"High\u2010Dimensional L\n                  2 Boosting: Rate of Convergence","author":"Luo Y.","year":"2016","journal-title":"arXiv Preprint arXiv:1602.08927"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1660173"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"key":"e_1_2_11_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asaa076"},{"issue":"3","key":"e_1_2_11_42_1","first-page":"61","article-title":"Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods","volume":"10","author":"Platt J.","year":"1999","journal-title":"Advances in Large Margin Classifiers"},{"key":"e_1_2_11_43_1","article-title":"Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score Based Estimators","author":"Rabenseifner J.","year":"2025","journal-title":"arXiv Preprint arXiv:2503.17290"},{"key":"e_1_2_11_44_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476493"},{"key":"e_1_2_11_45_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asz059"},{"key":"e_1_2_11_46_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOS1824"},{"key":"e_1_2_11_47_1","doi-asserted-by":"crossref","first-page":"107138","DOI":"10.52202\/079017-3402","article-title":"Self\u2010Calibrating Conformal Prediction","volume":"37","author":"Laan L.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_48_1","article-title":"Stabilized Inverse Probability Weighting via Isotonic Calibration","author":"Van der Laan L.","year":"2024","journal-title":"arXiv Preprint arXiv:2411.06342"},{"key":"e_1_2_11_49_1","article-title":"Automatic Doubly Robust Inference for Linear Functionals via Calibrated Debiased Machine Learning","author":"Van der Laan L.","year":"2024","journal-title":"arXiv Preprint arXiv:2411.02771"},{"key":"e_1_2_11_50_1","article-title":"Causal Isotonic Calibration for Heterogeneous Treatment Effects","author":"Van der Laan L.","year":"2023","journal-title":"arXiv Preprint arXiv:2302.14011"},{"key":"e_1_2_11_51_1","article-title":"Venn\u2010Abers Predictors","author":"Vovk V.","year":"2014","journal-title":"arXiv Preprint arXiv:1211.0025"},{"key":"e_1_2_11_52_1","article-title":"Large\u2010Scale Probabilistic Prediction With and Without Validity Guarantees","volume":"2015","author":"Vovk V.","year":"2015","journal-title":"In Proceedings of NIPS"},{"key":"e_1_2_11_53_1","article-title":"Adaptive Concentration of Regression Trees, With Application to Random Forests","author":"Wager S.","year":"2015","journal-title":"arXiv Preprint arXiv:1503.06388"},{"key":"e_1_2_11_54_1","article-title":"Calibration in Deep Learning: A Survey of the State\u2010Of\u2010The\u2010Art","author":"Wang C.","year":"2023","journal-title":"arXiv Preprint arXiv:2308.01222"},{"key":"e_1_2_11_55_1","first-page":"9280","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Xu Y.","year":"2022"},{"key":"e_1_2_11_56_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy008"},{"key":"e_1_2_11_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775151"},{"key":"e_1_2_11_58_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1021379864"}],"container-title":["Journal of Applied Econometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jae.70057","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jae.70057","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jae.70057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T16:45:56Z","timestamp":1785775556000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jae.70057"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["10.1002\/jae.70057"],"URL":"https:\/\/doi.org\/10.1002\/jae.70057","archive":["Portico"],"relation":{},"ISSN":["0883-7252","1099-1255"],"issn-type":[{"value":"0883-7252","type":"print"},{"value":"1099-1255","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"assertion":[{"value":"2025-01-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}