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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.
- Hernán, M.A. 2015. Invited commentary: Agent-based models for causal inference-reweighting data and theory in epidemiology. American Journal of Epidemiology 2181(2):103-5.
- Hernán, M.A, Hernández-Díaz, S., Werler, M.M., and Mitchell, A.A. 2002. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. American Journal of Epidemiology 155:176-84.
- Greenland, S., Pearl, .J, and Robins, J.M. Causal diagrams for epidemiologic research. Epidemiology 1999; 10(1):37-48.
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The Causal Effect of Parent–Child Interactions on Child Language Development at 3 and 4 Years
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Poppe, L., Paepe, A. L. D., Deforche, B. I., Dyck, D. V., Loeys, T., & Cauwenberg, J. V. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01723-w · Cited by 7
“Research questions can be categorized into three broad groups [2].”
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Post, R., Petković, M., van den Heuvel, I., & van den Heuvel, E. R. (2023) · Epidemiology · doi:10.1097/ede.0000000000001684 · Cited by 5
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BackgroundRucinski, K. B., Knight, J., Willis, K., Wang, L., Rao, A., Roach, M. A., Phaswana-Mafuya, R., Bao, L., Thiam, S., Arimi, P., et al. (2024) · Current HIV/AIDS Reports · doi:10.1007/s11904-024-00702-3 · Cited by 5
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Xiao, Z., Hauser, O., Kirkwood, C., Li, D. Z., Ford, T. J., & Higgins, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-73714-z · Cited by 4
“It focuses on the differences between two potential outcomes 38–40 , often an observed factual and an unobservable counterfactual outcome 41,42 .”
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Forrest, M. R., Weissgerber, T. L., Lieske, E. S., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000210169 · Cited by 4
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Colineaux, H., Lepage, B., Chauvin, P., Dimeglio, C., Delpierre, C., & Lefèvre, T. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22030348 · Cited by 3
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Kurthen, M., & Enßlin, T. A. (2019) · Entropy · doi:10.3390/e22010046 · Cited by 4
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“…understanding of data science as the use of coding to draw conclusions from diverse data sets by solving five classes of tasks (Adhikari et al., 2021; Hernán et al., 2019): • Data preprocessing—preparing data for analysis using techniques for data cleaning, data wrangling, and data transformation.”
BackgroundMethodHofstad, T., Nyttingnes, O., Markussen, S., Johnsen, E., Killackey, E. J., McDaid, D., Rinaldi, M., Dean, K., Brinchmann, B., Douglas, K. S., et al. (2023) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1980 · Cited by 4
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MethodTowards a unified approach to formal “risk of bias” assessments for causal and descriptive inference
Pescott, O. L., Boyd, R. J., Powney, G. D., & Stewart, G. B. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02687-0 · Cited by 8
“…types have often been distinguished by other researchers: Shmueli (2010) identified explanatory, predictive and descriptive aims for models, whilst Hernán et al. (2019) similarly distinguished between description, prediction and causal inference (see Carlin & Moreno-Betancur, 2025 for a recent…”
Scholz, M., & Bürkner, P. (2025) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2024.2449534 · Cited by 8
“The do -calculus [14] and equivalently the potential outcomes framework [15,16] offer sound theories to identify models that are able to provide an unbiased estimate of a parameter of choice.”
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Dee, L. E., Kimmel, K. A., & Hayden, M. T. (2022) · Wiley eBooks · doi:10.1002/9781119902911.ch6 · Cited by 4
Brocherie, F., Chassard, T., Toussaint, J., et al. (2022) · Sports Medicine · doi:10.1007/s40279-022-01736-6 · Cited by 4
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BackgroundFitzgerald, O., Perez‐Concha, O., Luxan, B. G., Rudd, L., & Jorm, L. R. (2023) · medRxiv · doi:10.1101/2023.04.20.23288845 · Cited by 2
Hossan, M. Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5378448 · Cited by 4
Thiesmeier, R., & Orsini, N. (2024) · JMIR Medical Education · doi:10.2196/52679 · Cited by 3
van Mierlo, R. F. R., Scheenstra, B., Verbeek, J. G. E., Bruninx, A., Kalendralis, P., Bermejo, I., Dekker, A. L. A. J., Hof, A. W. J. V. ’., Spreeuwenberg, M. D., & Hochstenbach, L. M. J. (2025) · JMIR Research Protocols · doi:10.2196/66068 · Cited by 3
“We developed the CPM [19] that estimates baseline 10-year ASCVD risk based on a set of conventional risk factors, individual characteristics, and medication.”
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Hafermann, L., Klein, N., Rauch, G., Kammer, M., & Heinze, G. (2022) · Entropy · doi:10.3390/e24060847 · Cited by 5
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BackgroundKurth, T., & Rohmann, J. L. (2023) · Cephalalgia · doi:10.1177/03331024221132007 · Cited by 3
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Alied, M., Law-Clucas, S., Allsop, M. J., Ramsenthaler, C., May, P., Bearne, A., Powell, M., Rosling, J., Kumar, R., Scerri, L., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-093175 · Cited by 3
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BackgroundHealth and evidence in health economics
Mullahy, J. (2019) · Health Economics · doi:10.1002/hec.3926 · Cited by 3
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Chioléro, A. (2019) · American Journal of Public Health · doi:10.2105/ajph.2019.305282 · Cited by 2
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Dahabreh, I. J. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwad014 · Cited by 3
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“As a consequence, there are limits as to what type of questions can reasonably be answered by certain types of data collection and methodological approaches [37 , 47 ].”
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InfluentialKeep asking: What do I want? What do I have? What do I do?
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“Using this approach for causal questions in epidemiology calls for specific kinds of background knowledge, data sources, and research tools [43, 47].”
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“…scenarios and hypothetical interventions, causal inference differs from description (i.e., summarising data features) and prediction (i.e., identifying patterns and forecasting), because it requires external knowledge of how the data came into being, known as the data generating process [19].”
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Moharrami, M., Watson, E., Huang, S., Madathil, S., Kim, J., McPartlin, A. J., Malik, N., Singhal, S., Hahn, E., Waldron, J. N., et al. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02359-4 · Cited by 2
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“Making causal inferences from observational data has drawn a lot of attention in the recent academic literature, e.g., Lederer et al. (2019), Hernan, Hsu, and Healy (2019).”
BackgroundLifetime employment trajectories and cancer
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“In this “prediction exercise” 72 , and thanks to sequence analysis, we found that, compared to women who spent their lives mainly caring for the home/family, four employment trajectories were associated with a higher risk of cancer over the life course: women who worked predominantly full-time,…”
BackgroundReilly, E. E., Brown, T. A., DeJesus, C. R., Kaye, W. H., & Wierenga, C. E. (2024) · International Journal of Eating Disorders · doi:10.1002/eat.24329 · Cited by 2
Siddle, H. J., Wilson, M., Nam, J. L., Garcia‐Montoya, L., Duquenne, L. M., Mankia, K. S., Emery, P., & Hensor, E. M. A. (2024) · Lara D. Veeken · doi:10.1093/rheumatology/keae653 · Cited by 2
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Regression—A Means, Not an End
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Hoberstorfer, T., Nopp, S., Steiner, D., Deinsberger, J., Schlager, O., Pabinger, I., Weber, B., & Ay, C. (2025) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2025.05.005 · Cited by 3
Teaching of Confidence Intervals in Context
Finch, S. J., & Gordon, I. R. (2025) · Australian & New Zealand Journal of Statistics · doi:10.1111/anzs.70024 · Cited by 2
“While modern introductory statistics curricula teach elements of simple design, the tasks of data science (Hernán et al. 2019) in applied practice may not map in a simple way to the design examples we teach.”
Natural Effects and Separable Effects: Insights into Mediation Analysis
Suzuki, E., Shinozaki, T., & Yamamoto, E. (2025) · Current Epidemiology Reports · doi:10.1007/s40471-025-00369-3 · Cited by 2
“When the counterfactual outcomes are not sufficiently well defined, observational data may still be quite useful for non-causal prediction [18, 56].”
Overvad, T. F., Skjøth, F., Albertsen, I. E., Larsen, T. B., Søgaard, M., & Lip, G. Y. H. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2020.05.042 · Cited by 2
Artificial Intelligence in Acute Ischemic Stroke
Werdiger, F., Bivard, A., & Parsons, M. W. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_287 · Cited by 2
Brennan, B. P., & Hudson, J. I. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.04.002 · Cited by 2
An Empirical Analysis of Vision Transformers Robustness to Spurious Correlations in Health Data
Lacerda, A. M., Ayala, D., Malaguth, F., & Kanadani, F. N. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191635 · Cited by 2
“Even though the automated diagnosis is widely addressed in the literature [8], the main approaches do not explore interpretability [13] and do not have well-defined causality structures [14].”
BackgroundCIMT 2024: Report on the 21st Annual Meeting of the Association for Cancer Immunotherapy
Ahrberg, Y., Dallmann, J., Freitag, J., Hassan, A., Jung, C., Kiefer, J., Muralidharan, A. M., Peter, M., & Beck, J. D. (2024) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2381925 · Cited by 2
Vansteelandt, S., & Steen, J. (2025) · Statistics in Medicine · doi:10.1002/sim.10312 · Cited by 2
Reynolds, C. A., & Chakraborty, P. (2026) · LGBT Health · doi:10.1177/23258292261462190 · Cited by 2
Quantum causal inference in the presence of hidden common causes: An entropic approach
Javidian, M. A., Aggarwal, V., & Jacob, Z. (2022) · Physical Review A · doi:10.1103/physreva.106.062425 · Cited by 1
“…Macken-zie, 2018): the conclusions drawn from scientific studies almost always involve extracting causation (cause and effect relationships) from association, even if researchers often refrain from explicitly acknowledging the causal goal of research projects (Hern´an, 2018; Hern´an et al., 2019).”
BackgroundTang, W., Spiegelman, D. L., Wu, Y., & Wang, M. (2026) · Statistics in Medicine · doi:10.1002/sim.70430 · Cited by 1
“This finding is particularly important when the research goal is to identify the causal effect rather than make predictions [49].”
Tibenderana, J. R. (2023) · International Journal of Surgery Global Health · doi:10.1097/gh9.0000000000000246 · Cited by 1
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.
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