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A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks
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For much of the recent history of science, learning from data was the academic realm of statistics,1,2 but in the early 20th century, the founders of modern statistics made a momentous decision abo...
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“ML can play a role in causal inference [41,340,619], including in the context of policy problems [40,453] and in climate-relevant scenarios such as estimating the effects of temperature on human mortality [356] and the effects of World Bank projects on vegetative cover [883].”
From Tackling climate change with machine learning (2023) · cites this paper as [340]
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Research works printed but not linked: 5
- Beam, A.L., and Kohane I.S. 2018. Big Data and Machine Learning in Health Care. JAMA 319(13):1,317-8.
- Robins, J.M., and Greenland, S. 1986. The role of model selection in causal inference from nonexperimental data. American Journal of Epidemiology 123(3):392-402.
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Metadata
- Authors
- Hernán, M. A., Hsu, J., & Healy, B.
- Affiliations
- Harvard University (US)
- ORCID
- Healy, Brian
- Venue
- CHANCE
- Publisher
- Informa UK Limited
- Published
- 2019-01-02
- Volume
- 32
- Issue
- 1
- Pages
- 42-49
- ISSN
- 0933-2480 (ISSN-L)
- 1867-2280
- OpenAlex
- W2798958557
- Cited by
- 495
- References
- 0
- Citations by year
- 2026: 70 · 2025: 79 · 2024: 67 · 2023: 70 · 2022: 64 · 2021: 67 · 2020: 56 · 2019: 20
- FWCI
- 40.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHANCE (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Keywords
- causal inference, history of statistics, learning from data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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