Chernozhukov, V., Demirer, M., Duflo, E., & Fernández-Val, I. [ca. 2018]. Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments. In NBER working paper series [Cd]. Cambridge, Mass.: National Bureau of Economic Research. https://doi.org/10.3386/w24678
ISO-690 (author-date, English)CHERNOZHUKOV, Victor, DEMIRER, Mert, DUFLO, Esther und FERNÁNDEZ-VAL, Iván, 2018. Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments. Cambridge, Mass.: National Bureau of Economic Research.
Modern Language Association 9th editionChernozhukov, V., M. Demirer, E. Duflo, und I. Fernández-Val. „Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments“. NBER working paper series, cd, National Bureau of Economic Research, 2018, https://doi.org/10.3386/w24678.
Mohr Siebeck - Recht (Deutsch - Österreich)Chernozhukov, Victor/Demirer, Mert/Duflo, Esther/Fernández-Val, Iván: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, Cambridge, Mass. 2018.
Emerald - HarvardChernozhukov, V., Demirer, M., Duflo, E. und Fernández-Val, I. (2018), Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, NBER working paper series, Bd. , National Bureau of Economic Research, Cambridge, Mass., verfügbar unter:https://doi.org/10.3386/w24678.