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Mullainathan and Spiess, “Machine Learning: An Applied Econometric Approach” (2017) bears on M04. Mullainathan and Spiess characterise machine learning as a prediction tool distinct from econometric parameter estimation, for use in empirical economics. Copy claim link
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Supporting evidence: primary · 1 source
Date 2017 · publication Role Context Register (kind of text) Nonfiction Missing element Treats prediction as what ML does methodologically, not as AI's economic function, and draws no consequences from a fall in its cost. Tier Tier 1. JEP (core venue). Scope and limits Abstract page only; full article not retrieved. Concerns machine learning as an econometric method, not AI's economic function.
The evidence 01 primary source Supports the claim
Machine Learning: An Applied Econometric Approach ↗ Journal of Economic Perspectives 31(2), Spring 2017, pp. 87–106 · 2017
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Source excerpt “Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation.” Location in source AEA article page, Abstract (JEP 31(2), Spring 2017, pp. 87-106)
Machine-checked · 2026-10-14
Source details and check notes Authors Sendhil Mullainathan; Jann Spiess Version 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 Canonical address Open the canonical source ↗ Accessed 2026-10-09 Language English (en) Attribution Sendhil Mullainathan, Jann Spiess, “Machine Learning: An Applied Econometric Approach”, Journal of Economic Perspectives 31(2), Spring 2017, pp. 87–106, 2017. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage M04-01 (research/economics-mainstream). The quotation (17 words) matched the retrieved current copy (SHA-256 eedb655b363848852d4300eaa5009a52ca3f0b9d44e1724a3d4202ea98ac2ab3) in the pack's verification. Re-checked at admission on 2026-10-14 against a copy retrieved that day, whose SHA-256 differs from the recorded one. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document bears on the proposition without meeting its criterion; the record names the missing element. It is not an articulation and implies no influence.
Date. AEA article page. Checked in a current copy; an archive capture was not available, so earlier bytes are not authenticated.
Record ID ec-m04-mullainathan-spiess-2017-machine-learning-econometric-b