Machine Learning: An Applied Econometric Approach
Machine Learning: An Applied Econometric Approach, by Sendhil Mullainathan, Jann Spiess. Journal of Economic Perspectives 31(2), Spring 2017, pp. 87–106.
- Issued by
- No issuing record yet. A missing record is a gap in coverage.
- Authors credited
- No authorship claim recorded yet. The source record lists Sendhil Mullainathan and Jann Spiess.
- Dates
- No date claim recorded yet. The source record gives the publication date as 2017.
- Exact source
- Machine Learning: An Applied Econometric Approach Journal of Economic Perspectives 31(2), Spring 2017, pp. 87–106 · published 2017 · Abstract page only. Current copy retrieved directly on 2026-10-09; no archive capture (Wayback unreachable), so earlier bytes are not authenticated. SHA-256 eedb655b363848852d4300eaa5009a52ca3f0b9d44e1724a3d4202ea98ac2ab3. Full text not retrieved; only the AEA abstract page. Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Appears in
- Economics of machine intelligence →1 record on this document
Versions
No other versions of this document are recorded. The evidence applies to the source version listed above. Other versions, such as a preprint, working paper or journal publication, are not identified or substituted.
Documentary relations
Each relation is listed separately. Citing, crediting and reporting the use of a method are different acts, and none of them shows agreement or influence by itself.
Bears on · 1
Propositions a passage comes close to without meeting the criterion; each record names what is missing.
Not recorded for this document: cites · cited by · explicitly credits · reports using a method from · method reported as used by · applies parts of · parts applied by · discusses · reports evaluation of. A missing record does not show that the relation does not exist.