00000nam 2200000zi 4500 9.943150 CaOODSP 20240913093735 m o d f cr cn||||||||| 240912e20240628oncd ob f000 0 eng d CaOODSP eng rda CaOODSP CS11-522/2022-1-17E-PDF Handouyahia, Andy, author. Heterogeneous casual effects of labour marketing programs : a machine learning approach / by Andy Handouyahia, Tristan Rikhi, Georges Awad and Essolaba Aouli. At head of title: Proceedings of Statistics Canada Symposium 2022 : data disaggregation : building a more representative data portrait of society [Ottawa] : Statistics Canada = Statistique Canada, June 28, 2024. 1 online resource (7 unnumbered pages) : graphs. text txt rdacontent computer c rdamedia online resource cr rdacarrier [Statistics Canada international symposium series : proceedings], 1709-8211 Cover title. Issued also in French under title: Effets causals hétérogènes des programmes du marché du travail : une approche d'apprentissage automatique. Includes bibliographical references. "In this paper, we look for presence of heterogeneity in conducting impact evaluations of the Skills Development intervention delivered under the Labour Market Development Agreements. We used linked longitudinal administrative data covering a sample of Skills Development participants from 2010 to 2017. We apply a causal machine-learning estimator as in Lechner (2019) to estimate the individualized program impacts at the finest aggregation level. These granular impacts reveal the distribution of net impacts facilitating further investigation as to what works for whom. The findings suggest statistically significant improvements in labour market outcomes for participants overall and for subgroups of policy interest"--Abstract. Labor market Econometric models. Machine learning. Statistics Canada, issuing body. Effets causals hétérogènes des programmes du marché du travail : (CaOODSP)9.943155 Statistics Canada international symposium series - proceedings, 1709-8211 (CaOODSP)9.505681 PDF 560 KB https://publications.gc.ca/collections/collection_2024/statcan/11-522-x/CS11-522-2022-1-17-eng.pdf 11-522-X