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Regression modelQuasi-experimental / causal inference

Tofauti-ndani-ya-Tofauti (DiD) Iliyoimarishwa na Mashine ya Kujifunza (ML-DiD)

DiD Iliyoimarishwa na Mashine ya Kujifunza inachanganya mkakati wa kitambo wa kitambulisho cha tofauti-ndani-ya-tofauti na makadirio rahisi ya ML kwa ajili ya kazi za usumbufu — alama ya tabia na urejesho wa matokeo — ili kupata makadirio halali ya kisababishi hata pale uteuzi wa matibabu na mienendo ya matokeo yanapokuwa magumu, yenye vipimo vingi, au yasiyo ya mstari. Njia hii, iliyoandikwa katika kujifunza kwa mashine mara mbili/iliyofutwa makosa (Chernozhukov et al., 2018) na DiD yenye ulinzi mara mbili (Sant'Anna & Zhao, 2020), inalinda dhidi ya upotoshaji wa kukosa vipimo huku ikihifadhi mantiki ya msingi ya DiD ya kabla-baada, iliyotibiwa dhidi ya ulinganisho wa udhibiti.

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Vyanzo

  1. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI: 10.1111/ectj.12097
  2. Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI: 10.1016/j.jeconom.2020.12.001

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Machine Learning-Augmented Difference-in-Differences Estimator. ScholarGate. https://scholargate.app/sw/causal-inference/machine-learning-augmented-difference-in-differences

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Imerejelewa na

ScholarGateMachine learning-augmented difference-in-differences (Machine Learning-Augmented Difference-in-Differences Estimator). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/causal-inference/machine-learning-augmented-difference-in-differences · Seti ya data: https://doi.org/10.5281/zenodo.20539026