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Our approach is based on weighting observations by the inverse of two versions of the generalized propensity score (GPS), namely the conditional density of treatment either given observed covariates or given covariates and the mediator. Our effect estimators are shown to be asymptotically normal when the GPS is estimated by either a parametric or a nonparametric kernel\u2010based method. We also provide a simulation study and an empirical illustration based on the Job Corps experimental study.<\/jats:p>","DOI":"10.1002\/jae.2765","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T02:29:58Z","timestamp":1586917798000},"page":"814-840","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Direct and indirect effects of continuous treatments based on generalized propensity score weighting"],"prefix":"10.1002","volume":"35","author":[{"given":"Martin","family":"Huber","sequence":"first","affiliation":[{"name":"Department of Economics University of Fribourg Fribourg Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu\u2010Chin","family":"Hsu","sequence":"additional","affiliation":[{"name":"Academia Sinica Institute of Economics, National Central University Taoyuan City Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying\u2010Ying","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Economics University of California Irvine Irvine California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Layal","family":"Lettry","sequence":"additional","affiliation":[{"name":"Swiss Federal Agency for Social Insurances Bern Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,5,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2014.975555"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3016"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2010.01547.x"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.51.6.1173"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10260-012-0193-4"},{"key":"e_1_2_9_7_1","unstructured":"Bodory H. &Huber M.(2018).The causalweight package for causal inference in r\u00a0(SES Working Paper 493). 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