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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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Spinal pain in childhood: prevalence, trajectories, and diagnoses in children 6 to 17 years of age
Hébert, J. J., Beynon, A. M., Jones, B. L., Wang, C., Shrier, I., Hartvigsen, J., Leboeuf‐Yde, C., Hestbæk, L., Swain, M. S., Junge, T., et al. (2022) · European Journal of Pediatrics · doi:10.1007/s00431-021-04369-5 · Cited by 25
“Data science tasks in epidemiology can be classified as description, prediction, and causal inference [33], and future research is needed to investigate these aspects of spinal pain in children.”
BackgroundAnalytical Problem Solving Based on Causal, Correlational and Deductive Models
de Mast, J., Steiner, S. H., Nuijten, W., & Kapitan, D. (2022) · The American Statistician · doi:10.1080/00031305.2021.2023633 · Cited by 21
Fox, M. P., Edwards, J. K., Platt, R. W., & Balzer, L. B. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz233 · Cited by 22
Mykletun, A., Widding‐Havneraas, T., Chaulagain, A., Lyhmann, I., Bjelland, I., Halmøy, A., Elwert, F., Butterworth, P., Markussen, S., Zachrisson, H. D., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-041698 · Cited by 26
Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV
Dickerman, B. A., Dahabreh, I. J., Cantos, K., Logan, R. W., Lodi, S., Rentsch, C. T., Justice, A. C., & Hernán, M. Á. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-022-00855-8 · Cited by 20
Self-citationWyss, R., Yanover, C., El‐Hay, T., Bennett, D., Platt, R. W., Zullo, A. R., Sari, G., Wen, X., Ye, Y., Yuan, H., et al. (2022) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5500 · Cited by 22
“74 ,75-77 Below, we survey a list of standard ML diagnostics for model prediction and diagnostic for causal inference with a focus on assessing the consistency and accuracy of models for high-dimensional proxy adjustment.”
BackgroundDyer, B. P. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111960 · Cited by 23
Benbow, J. H., Rivera, D. R. R., Lund, J. L., Feldman, J., & Kim, E. S. (2022) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_350574 · Cited by 23
Fahmi, A., Wong, D., Walker, L. E., Buchan, I. E., Pirmohamed, M., Sharma, A. K., Cant, H. E. O., Ashcroft, D. M., & van Staa, T. P. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281466 · Cited by 24
“Like most risk prediction models, the results of this study should not be used for counterfactual risk prediction and causal inference [39].”
The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization
Chatton, A., & Rohrer, J. M. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459241236149 · Cited by 39
“This type of bias is common to all causal methods and requires expert knowledge (Hernán et al., 2019); sometimes, it may even require one to target a different theoretical estimand altogether.”
BackgroundMsweli, N. T., Mawela, T., & Twinomurinzi, H. (2023) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2023.100628 · Cited by 22
Nielsen, R. O., Bertelsen, M. L., Møller, M., et al. (2020) · British Journal of Sports Medicine · doi:10.1136/bjsports-2018-100245 · Cited by 21
Clinical decision support for ExtraCorporeal Membrane Oxygenation: Will we fly by wire?
Pladet, L. C. A., Luijken, K., Fresiello, L., Miranda, D. D. R., Hermens, J. A. J. M., van Smeden, M., Cremer, O. L., Donker, D. W., & Meuwese, C. L. (2023) · Perfusion · doi:10.1177/02676591231163688 · Cited by 23
From persons to general principles: Methodological decisions for idiographic and nomothetic research
Kuper, N., Andresen, P. K., Beck, E. D., Costantini, G., Hamaker, E. L., Wright, A. G. C., & Zimmermann, J. (2024) · European Journal of Personality · doi:10.1177/08902070241278020 · Cited by 34
“Several distinctions have been proposed (e.g., Hamaker et al., 2020; Hern´an et al., 2019; Mõttus et al., 2020; Shmueli, 2010).”
Aubert, A. M., Chen, L., Shivappa, N., Cooper, C. C., Crozier, S., Duijts, L., Forhan, A., Hanke, W., Harvey, N. C., Jankowska, A., et al. (2022) · Clinical Nutrition · doi:10.1016/j.clnu.2022.06.042 · Cited by 22
“[23], where the term “predictor” is used, as a synonym of “factors associated with”, for variables that statistically predict women’s dietary quality, regardless of whether it implies causation.”
Nielsen, R., Simonsen, N., Casals, M., Stamatakis, E., & Mansournia, M. (2020) · British Journal of Sports Medicine · doi:10.1136/bjsports-2020-102144 · Cited by 22
On the Uses and Abuses of Regression Models: A Call for Reform of Statistical Practice and Teaching
Carlin, J. B., & Moreno‐Betancur, M. (2025) · Statistics in Medicine · doi:10.1002/sim.10244 · Cited by 37
“If the descriptive question instead seeks to describe the difference in mean (or risk, or prevalence, for a binary outcome) across subgroups, say in the simplest case of a binary subgroup indicator variable XX, coded 0/1, then the regression in (1) can be used as it stands, with coefficient ββ1 representing that diffe…”
InfluentialCausal inference in dentistry: Time to move forward
Schuch, H. S., Nascimento, G. G., Demarco, F. F., & Haag, D. G. (2023) · Community Dentistry And Oral Epidemiology · doi:10.1111/cdoe.12802 · Cited by 19
Boer, O. D., Marroun, H. E., & Muetzel, R. L. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02471-2 · Cited by 24
Ponkilainen, V., Uimonen, M., Raittio, L., Kuitunen, I., Eskelinen, A. P., & Reito, A. R. (2021) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2021.03.020 · Cited by 17
Abécassis, J., Dumas, É., Alberge, J., & Varoquaux, G. P. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095750 · Cited by 26
Methodological Gaps in Studying the Oral-Systemic Disease Connection
Raittio, E. J., & Farmer, J. W. (2021) · Journal of Dental Research · doi:10.1177/0022034520982972 · Cited by 17
“For example, known predictors and causes should be controlled for in order to obtain proper estimates of the relationships of interest (Hernán et al. 2019; Conroy and Murray 2020).”
BackgroundPavlović, M., Hajj, G. S. A., Kanduri, C., Pensar, J., Wood, M. E., Sollid, L. M., Greiff, V., & Sandve, G. K. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00781-8 · Cited by 29
“For predictive purposes, confounding is not generally problematic and might even improve the performance of the model 48,49 .”
Stroke Risk in Survivors of Head and Neck Cancer
Yip, P. L., Zheng, H., Cheo, T., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.54947 · Cited by 17
The role of causal inference in health services research I: tasks in health services research
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Patterns and Drivers of Household Sanitation Access and Sustainability in Kwale County, Kenya
Legge, H., Halliday, K. E., Kepha, S., Mcharo, C., Witek-McManus, S. S., El‐Busaidy, H., Muendo, R., Safari, T., Mwandawiro, C. S., Matendechero, S. H., et al. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.0c05647 · Cited by 17
Diet quality is not associated with late-onset multiple sclerosis risk– A Danish Cohort Study
Pommerich, U. M., Nielsen, R. O., Overvad, K., Dahm, C. C., Tjønneland, A. M., Olsen, A., & Dalgas, U. (2020) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2020.101968 · Cited by 16
Álvarez‐Gálvez, J., Suárez-Lledó, V., Salvador‐Carulla, L., & Barrios, J. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247759 · Cited by 15
“Social and economic determinants of suicides Once we have reached this point, one inevitable research question is: which are the social and economic factors behind increasing suicides after the 2011 recession period? Many studies have generally focused on unemployment as one of the most relevant causes of suicides aft…”
BackgroundResultPainter, J. L., Ramcharran, D., & Bate, A. (2025) · Therapeutic Advances in Drug Safety · doi:10.1177/20420986251332743 · Cited by 19
“In addition, the interpretation of study diagnostics to inform study design choices and ultimately choose a design based on a framework that enables causal inference 82 may be supported with GenAI.”
Introducing Causal Inference Using Bayesian Networks and do-Calculus
Lu, Y., Zheng, Q., & Quinn, D. (2022) · Journal of Statistics and Data Science Education · doi:10.1080/26939169.2022.2128118 · Cited by 15
“In recent years, increasing literature on statistics education has advocated for integrating causal inference into statistics curriculum (e.g., Ridgway 2016; Kaplan 2018; Hernán, Hsu, and Healy 2019; Adams et al. 2021; Cummiskey et al. 2020; Lübke et al. 2020; Pearl and Lesser 2021).”
BackgroundKim, C., Choi, Y. H., Choi, J. Y., et al. (2023) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2023.106966 · Cited by 15
Living with observational data in biological anthropology
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Biostatistics: a fundamental discipline at the core of modern health data science
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“For example, in the case of INSPIRE registry discussed earlier in Points 2 and 8, were a causal inference question about the effect of a reperfusion therapy on the functional outcome to be asked, the probability of achieving a predefined level of function (say, mRS0-2) would have to be estimated…”
InfluentialA Causal Inference Framework for Climate Change Attribution in Ecology
Dudney, J. C., Dee, L. E., Heilmayr, R., Byrnes, J. E. K., & Siegel, K. (2025) · Ecology Letters · doi:10.1111/ele.70192 · Cited by 21
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“The first problem is the confusion between surveillance and research [1, 11].”
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“E”
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Review of machine learning solutions for eating disorders
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“Moreover, datasets found in actual clinical settings are prone to selection biases (being observational), which complicates the direct application of CMs for computing CI [133,150–152].”
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“Thus, caution is warranted in the interpretation of specific associations in our models, since the goal of our analysis is predictive, rather than causal.(39) For instance, inclusion in the model of any downstream variable in a causal pathway starting with RRF can potentially lead to collider bias, which can reverse t…”
BackgroundToward a causal model of chronic back pain: Challenges and opportunities
Huie, J. R., Vashisht, R., Galivanche, A. R., Hadjadj, C., Morshed, S., Butte, A. J., Ferguson, A. R., & O'Neill, C. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.1017412 · Cited by 10
Collaborative causal inference on distributed data
Kawamata, Y., Motai, R., Okada, Y., Imakura, A., & Sakurai, T. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123024 · Cited by 13
“Statistical causal inference, based on Hern´an et al. (2019), is the statistical prediction of outcomes (outputs) if treatments (inputs) in the world were different.”
BackgroundMalvaso, C., Montgomerie, A., Pilkington, R. M., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-057284 · Cited by 12
Invited Commentary: Treatment Drop-in—Making the Case for Causal Prediction
Sperrin, M., Diaz‐Ordaz, K., & Pajouheshnia, R. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab030 · Cited by 12
Ten simple rules for building and maintaining a responsible data science workflow
Stoudt, S., Jernite, Y., Marshall, B., Marwick, B., Sharan, M., Whitaker, K., & Danchev, V. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012232 · Cited by 11
Kezios, K. (2021) · Epidemiologic Reviews · doi:10.1093/epirev/mxab008 · Cited by 12
So many variables, but what causes what?
CAT, C. D., & Unsworth, S. (2023) · Journal of Child Language · doi:10.1017/s0305000923000107 · Cited by 11
Walschots, Q., Zarchev, M. G., Unkel, M., & Kamperman, A. M. (2024) · Algorithms · doi:10.3390/a17090408 · Cited by 11
Distinguishing Description, Prediction, and Causal Inference
Ito, C., Al‐Hassany, L., Kurth, T., & Glatz, T. (2025) · Neurology · doi:10.1212/wnl.0000000000210171 · Cited by 14
“3,4 We provide simpli fi ed examples of research questions in neurology that may be asked within these domains in Table 1.”
Dyer, B. P. (2025) · European Heart Journal Open · doi:10.1093/ehjopen/oeaf070 · Cited by 12
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Langeslag, J. F., Berendse, K., Daams, J. G., Onland, W., Leeflang, M., van Kaam, A. H., & de Haan, T. R. (2023) · Neonatology · doi:10.1159/000530411 · Cited by 12
“The QUIPS domain confounding was excluded because confounding is a causal concept and correction for confounding in predictive studies could lead to bias by over fi tting the prediction [15, 16].”
BackgroundCurrent knowledge and availability of machine learning across the spectrum of trauma science
Gauss, T., Perkins, Z., & Tjardes, T. (2023) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000001104 · Cited by 10
Gvozdenović, E., Malvisi, L., Cinconze, E., Vansteelandt, S., Nakanwagi, P., Aris, E., & Rosillon, D. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01220-1 · Cited by 12
A structural characterization of shortcut features for prediction
Bellamy, D., Hernán, M. Á., & Beam, A. L. (2022) · European Journal of Epidemiology · doi:10.1007/s10654-022-00892-3 · Cited by 13
“Data analyses can be carried out for 3 tasks: description, prediction, and counterfactual prediction; the latter is used for causal inference [1].”
MethodSelf-citationOn the Selection of Item Scores or Composite Scores for Clinical Prediction
McClure, K. E., Ammerman, B. A., & Jacobucci, R. (2024) · Multivariate Behavioral Research · doi:10.1080/00273171.2023.2292598 · Cited by 19
“Conversely, predictive modeling aims to approximate underlying generative processes by training models to capture robust patterns in the data that accurately link predictors to outcomes in new samples (Hamaker et al., 2020; Hern � an et al., 2019; Shmueli, 2010).”
BackgroundBig Data and Systems Methods: The Next Frontier to Tackling the Global Obesity Epidemic
Lee, B. Y., Ferguson, M. C., Cox, S. N., et al. (2021) · Obesity · doi:10.1002/oby.23062 · Cited by 10
“Without accounting for these intricacies, such techniques yield limited insights on cause-and-effect relationships because traditional data collection methods still lack an agreed upon and accurate framework for drawing causal connections (7).”
BackgroundCadiou, S., Basagaña, X., González, J. R., Lepeule, J., Vrijheid, M., Siroux, V., & Slama, R. (2021) · Environment International · doi:10.1016/j.envint.2021.106509 · Cited by 13
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Watson, M. L., Hickman, S. H. M., Dreesbeimdiek, K. M., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281259 · Cited by 9
“Historically, healthcare researchers have refrained from explicitly seeking such findings [2], with an emphasis on the randomised controlled trial (RCT) as the only methodological framework capable of supporting such claims [1].”
BackgroundRaita, Y., Camargo, C. A., Liang, L., & Hasegawa, K. (2021) · Expert Review of Respiratory Medicine · doi:10.1080/17476348.2021.1913061 · Cited by 11
“This integration in data science qualitatively transforms ‘intelligence’ [7],”
BackgroundHow and when to use causal and associational language
Labrecque, J. A., & Kezios, K. (2026) · BMJ · doi:10.1136/bmj-2025-085749 · Cited by 12
Alvarez-Vargas, D., Braithwaite, D. W., Lortie‐Forgues, H., Moore, M. M., Castro, M., Wan, S., Martin, E. A., & Bailey, D. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286403 · Cited by 13
“For this reason, several have argued that psychologists’ reluctance to explicitly talk about causality limits the questions they can address directly, and results in seemingly contradictory findings, stifling cumulative progress in psychology [1, 13, 14, 24, 25].”
BackgroundResultHeat, humidity and health impacts: how causal diagrams can help tell the complex story
Sivaraj, S., Jakob Zscheischler, A., Buzan, J., Martius, O., Brönnimann, S., & Vicedo-Cabrera, A. M. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad5a25 · Cited by 11
“This framework imposes restrictions on the nature of queries to mostly causal effects of manip-ulable variables, through deliberate human intervention, on the outcome of interest [54, 57].”
BackgroundCausal inference in physical activity epidemiology research: an opportunity for better evidence
Ding, D., Tarp, J., Giovannucci, E. L., & Clare, P. J. (2025) · British Journal of Sports Medicine · doi:10.1136/bjsports-2024-109102 · Cited by 11
“20 21 Editorial causal inference frameworks or methods across all areas of physical activity research.”
Performance of prediction models for COVID-19: the Caudine Forks of the external validation
Martin, G., Sperrin, M., & Sotgiu, G. (2020) · European Respiratory Journal · doi:10.1183/13993003.03728-2020 · Cited by 9
“A potential solution could be the exploration of counterfactual prediction [19,20], in which the risk is estimated under fixed care regimes.”
BackgroundTransforming research methods education through data science literacy
Overton, M., & Kleinschmit, S. W. (2022) · Teaching Public Administration · doi:10.1177/01447394221084488 · Cited by 9
“Although some view data science solely as an analytical domain (Hern´an et al., 2019), the broader discipline is engaged with the process required to turn data into insight (Provost and Fawcett, 2013; Donoho, 2017).”
BackgroundProof of Concept Example for Use of Simulation to Allow Data Pooling Despite Privacy Restrictions
Filshtein, T. J., Li, X., Zimmerman, S. C., Ackley, S. F., Glymour, M. M., & Power, M. C. (2021) · Epidemiology · doi:10.1097/ede.0000000000001373 · Cited by 8
Research and training recommendations for public health data science
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Chen, S. Z., Zainal, N. H., & Newman, M. G. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.12.083 · Cited by 9
“Thus, there is a need for longitudinal studies to understand the temporal relations between aspects of self-construal and common mental disorders and to establish weak causal inferences (Hern ´ an et al., 2019).”
BackgroundWiehn, J., Kurth, T., Ravens‐Sieberer, U., Prugger, C., Piccininni, M., & Reiß, F. (2024) · Frontiers in Pediatrics · doi:10.3389/fped.2024.1252964 · Cited by 8
“The DAG is schematically represented in Figure 2.”
BackgroundMethodInfluentialMessinger, C. J., Bateman, B. T., & Wanis, K. N. (2025) · Anesthesiology · doi:10.1097/aln.0000000000005308 · Cited by 9
Plug-and-play use of tree-based methods: consequences for clinical prediction modeling
Meijerink, L., Schuit, E., Moons, K. G. M., & Leeuwenberg, A. M. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111834 · Cited by 10
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“However, it was chosen not to consider interactions between variables since this study is not examining causal pathways [11, 66].”
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Machine and deep learning in MS research are just powerful statistics – No
Healy, B. C. (2021) · Multiple Sclerosis Journal · doi:10.1177/1352458520978648 · Cited by 8
Self-citationWiedermann, W., & Sebastian, J. L. (2019) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1659127 · Cited by 8
Shi, X., Zhao, W., Chen, T., Yang, C., & Du, J. (2025) · Nature Communications · doi:10.1038/s41467-025-62783-x · Cited by 12
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“Formally this is the language of defining questions, estimands and estimators (Hernán, Hsu, and Healy 2019; Glass 2010).”
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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
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