The DOI matches its Crossref record; 60 of the 62 references it cites were found in a registry or an index.
Statistics and Causal Inference
Abstract
Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.
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In the citing paper
“As is usually the case, due to the fundamental problem of causal inference (Holland, 1986), we only observe one of the two potential outcomes for each unit i .”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Holland, 1986
In the citing paper
“causal effects can never be observed directly”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Holland 1986
In the citing paper
“In uplift modeling, however, the actual outcome that is estimated is unobserved, and therefore the error made by the model cannot be measured at the individual customer level.”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Historical data containing information on observed customer behavior never allows classifying customers in the four groups shown in Table 1, as the net e ↵ ect of treatment cannot be observed for an individual customer due to the fundamental problem of causal inference [34].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Uplift or, in other words, whether a customer is persuadable, a lost cause, do-notdisturb or a sure thing, cannot be observed due to the fundamental problem of causal inference [34].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Since the antecedent variable is an experimental variable in a randomized study, its causal relation to the mediator and the outcome variable can be interpreted unambiguously102,103.”
From Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning (2022) · cites this paper as [102]
In the citing paper
“Key to the ability to make inferences about mean causal effects are the assumptions of unit homogeneity and independence of units (Holland, 1986; King, Keohane and Verba, 1994: 91-97).”
From Multi-Method Research in the Social Sciences: A Review of Recent Frameworks and a Way Forward (2020) · cites this paper as Holland, 1986
In the citing paper
“When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980;Holland, 1986;Marsh et al., 2018;Mega et al., 2014).”
From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as Holland, 1986
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,804
The Environment and Disease: Association or Causation?
Hill, A. B. (1965) · Proceedings of the Royal Society of Medicine · doi:10.1177/003591576505800503 · Cited by 6,974
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Journal of The American Statistical Association
Fienberg, S. E. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,289
An Essay concerning Human Understanding
Locke, J. (1690) · Oxford University Press eBooks · doi:10.1093/oseo/instance.00018020 · Cited by 5,214
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478078 · Cited by 3,082
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Research works printed but not linked: 5
- 10.1016/B978-0-12-224150-5.50005-8 · DOI
- Hume , D. 1740 . A Treatise on Human Nature
- 10.1080/00031305.1985.10479383 · DOI
- 10.1080/01621459.1980.10477517 · DOI · Paper page
- Saris , W. and Stronkhorst , H. 1984 . Causal Modelling in Non-experimental Research , Amsterdam : Sociometric Research Foundation .
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Druckman, J. N. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511921452 · Cited by 622
Understanding and Using Mediators and Moderators
Wu, A. D., & Zumbo, B. D. (2007) · Social Indicators Research · doi:10.1007/s11205-007-9143-1 · Cited by 602
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After KKV: The New Methodology of Qualitative Research
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Meat consumption and risk of colorectal cancer.
Chao, A., Thun, M., Connell, C., McCullough, M., Jacobs, E., Flanders, W., Rodríguez, C., Sinha, R., & Calle, E. (2005) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.293.2.172 · Cited by 569
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Hsiang, S. (2016) · Annual Review of Resource Economics · doi:10.1146/annurev-resource-100815-095343 · Cited by 573
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Wodtke, G., Harding, D. J., & Elwert, F. (2011) · American Sociological Review · doi:10.1177/0003122411420816 · Cited by 474
VanderWeele, T. J., & Robinson, W. R. (2014) · Epidemiology · doi:10.1097/ede.0000000000000105 · Cited by 567
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Robins, J. M. (2000) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4612-1284-3_2 · Cited by 557
Heckman, J. J., & Vytlacil, E. (2007) · Handbook of econometrics · doi:10.1016/s1573-4412(07)06070-9 · Cited by 548
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Bayesian inference for causal effects in randomized experiments with noncompliance
Imbens, G. W., & Rubin, D. B. (1997) · The Annals of Statistics · doi:10.1214/aos/1034276631 · Cited by 538
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Stuart, E. A., Huskamp, H. A., Duckworth, K. S., Simmons, J. B., Song, Z., Chernew, M. E., & Barry, C. L. (2014) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-014-0123-z · Cited by 566
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Average causal effects from nonrandomized studies: A practical guide and simulated example.
Schafer, J. L., & Kang, J. (2008) · Psychological Methods · doi:10.1037/a0014268 · Cited by 550
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Ravallion, M. D. (2005) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-3625 · Cited by 519
Hsiang, S., & Jina, A. (2014) · National Bureau of Economic Research · doi:10.3386/w20352 · Cited by 547
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Bucy, E. P., & Holbert, R. L. (2010) · Medical Entomology and Zoology · Cited by 504
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Uggen, C., & Manza, J. (2002) · American Sociological Review · doi:10.1177/000312240206700601 · Cited by 497
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Hernán, M. Á., Brumback, B., & Robins, J. M. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753168154 · Cited by 483
Curtis, L. H., Hammill, B. G., Eisenstein, E. L., Kramer, J. M., & Anstrom, K. J. (2007) · Medical Care · doi:10.1097/mlr.0b013e31806518ac · Cited by 494
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
Kent, D. M., Steyerberg, E. W., & Klaveren, D. V. (2018) · BMJ · doi:10.1136/bmj.k4245 · Cited by 518
The importance of covariate selection in controlling for selection bias in observational studies.
Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H. (2010) · Psychological Methods · doi:10.1037/a0018719 · Cited by 490
Formal mode of statistical inference for causal effects
Rubin, D. B. (1990) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(90)90077-8 · Cited by 483
Principles of multilevel modelling.
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.1.158 · Cited by 500
Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000733 · Cited by 479
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Gerring, J. (2012) · British Journal of Political Science · doi:10.1017/s0007123412000130 · Cited by 507
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Eberhardt, L. L., & Thomas, J. M. (1991) · Ecological Monographs · doi:10.2307/1942999 · Cited by 455
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Maldonado, G., & Greenland, S. (2002) · International Journal of Epidemiology · doi:10.1093/intjepid/31.2.422 · Cited by 396
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Wells, K. B. (1999) · American Journal of Psychiatry · doi:10.1176/ajp.156.1.5 · Cited by 390
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Zhao, Z. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023705 · Cited by 389
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Bayesian perspectives for epidemiological research: I. Foundations and basic methods
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Angrist, J. D., Graddy, K., & Imbens, G. W. (2000) · The Review of Economic Studies · doi:10.1111/1467-937x.00141 · Cited by 372
As Others See Us: A Case Study in Path Analysis
Freedman, D. A. (1987) · Journal of Educational Statistics · doi:10.3102/10769986012002101 · Cited by 372
Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon
West, T. A. P., Börner, J., Sills, E. O., & Kontoleon, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004334117 · Cited by 372
Lechner, M. (2002) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/1467-985x.0asp2 · Cited by 349
Earnings and Employment Effects of Continuous Gff-the-Job Training in East Germany After Unification
Lechner, M. (1999) · Journal of Business and Economic Statistics · doi:10.1080/07350015.1999.10524798 · Cited by 428
Kang, J., & Schafer, J. L. (2007) · Statistical Science · Cited by 368
6. Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies
Smith, H. L. (1997) · Sociological Methodology · doi:10.1111/1467-9531.271030 · Cited by 364
Taste‐Based or Statistical Discrimination: The Economics of Discrimination Returns to its Roots
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Metadata
- Authors
- Holland, P. W.
- Affiliations
- Educational Testing Service (US)
- Corresponding author
- Holland, Paul W.
- ORCID
- Holland, Paul W.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1986-12
- Volume
- 81
- Issue
- 396
- Pages
- 945-960
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1978108654
- Cited by
- 5,293
- References
- 62
- Citations by year
- 2026: 188 · 2025: 260 · 2024: 246 · 2023: 266 · 2022: 274 · 2021: 323 · 2020: 302 · 2019: 269 · 2018: 259 · 2017: 202 · 2016: 212 · 2015: 229 · 2014: 271 · 2013: 224 · 2012: 187
- FWCI
- 14.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causal inference, causal models, correlation versus causation, randomized experiments, counterfactual reasoning, philosophy of statistics
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