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 .
Cited by
Sorted by citations
Items 201–300 of 1,019
Emsley, R., Dunn, G., & White, I. R. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280209105014 · Cited by 356
Propensity Score Matching Methods for Non-Experimental Causal StudiesPreprint
Rajeev, D., & Wahba, S. (1999) · SSRN Electronic Journal · doi:10.2139/ssrn.138259 · Cited by 445
Causal machine learning for predicting treatment outcomes
Feuerriegel, S., Frauen, D., Melnychuk, V., Schweisthal, J., Heß, K., Curth, A., Bauer, S., Kilbertus, N., Kohane, I. S., & van der Schaar, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-02902-1 · Cited by 353
On class differentials in educational attainment
Erikson, R. S., Goldthorpe, J. H., Jackson, M., Yaish, M., & Cox, D. R. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502433102 · Cited by 352
Mediation analysis methods used in observational research: a scoping review and recommendations
Rijnhart, J. J. M., Lamp, S. J., Valente, M. J., MacKinnon, D. P., Twisk, J. W. R., & Heymans, M. W. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01426-3 · Cited by 412
Causal Inference Without Counterfactuals
Dawid, A. P. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474210 · Cited by 414
Cattaneo, M. D., Frandsen, B., & Titiunik, R. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0010 · Cited by 350
Low P-Values or Narrow Confidence Intervals: Which Are More Durable?
Poole, C. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00005 · Cited by 363
The Relative Validity of Inferences About Mediation as a Function of Research Design Characteristics
Stone‐Romero, E. F., & Rosopa, P. J. (2007) · Organizational Research Methods · doi:10.1177/1094428107300342 · Cited by 349
Best Practices in Quasi–Experimental Designs: Matching Methods for Causal Inference
Stuart, E. A., & Rubin, D. B. (2008) · Sage eBooks · doi:10.4135/9781412995627.d14 · Cited by 356
Aronow, P. M., & Samii, C. (2017) · The Annals of Applied Statistics · doi:10.1214/16-aoas1005 · Cited by 347
Dynamic discrete choice and dynamic treatment effects
Heckman, J. J., & Navarro, S. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.11.002 · Cited by 354
Ham, J. C., & Lalonde, R. J. (1996) · Econometrica · doi:10.2307/2171928 · Cited by 344
Toward a Unified Theory of Causality
Mahoney, J. (2008) · Comparative Political Studies · doi:10.1177/0010414007313115 · Cited by 343
Hernán, M. Á., Brumback, B., & Robins, J. M. (2002) · Statistics in Medicine · doi:10.1002/sim.1144 · Cited by 335
Blundell, R. W., Dearden, L., & Sianesi, B. (2005) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2004.00360.x · Cited by 338
Climate, conflict, and social stability: what does the evidence say?
Hsiang, S. M., & Burke, M. (2014) · Climatic Change · doi:10.1007/s10584-013-0868-3 · Cited by 342
The Bradford Hill considerations on causality: a counterfactual perspective
Höfler, M. (2005) · Emerging Themes in Epidemiology · doi:10.1186/1742-7622-2-11 · Cited by 339
Graphs, Causality, and Structural Equation Models
PEARL, J. (1998) · Sociological Methods & Research · doi:10.1177/0049124198027002004 · Cited by 341
Assessing the effect of an influenza vaccine in an encouragement design
Hirano, K., Imbens, G. W., Rubin, D. B., & Zhou, X. (2000) · Biostatistics · doi:10.1093/biostatistics/1.1.69 · Cited by 347
Causal mapping of human brain function
Siddiqi, S., Kording, K. P., Parvizi, J., & Fox, M. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00583-8 · Cited by 368
Applications of multiple imputation in medical studies: from AIDS to NHANES
Barnard, J., & Meng, X.-L. (1999) · Statistical Methods in Medical Research · doi:10.1177/096228029900800103 · Cited by 340
Fish Consumption and Risk of Stroke in Men
He, K. (2002) · JAMA · doi:10.1001/jama.288.24.3130 · Cited by 340
Causal inference with observational data
Nichols, A. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700403 · Cited by 339
Causal Inference without Counterfactuals
Dawid, A. P. (2001) · Applied logic series · doi:10.1007/978-94-017-1586-7_3 · Cited by 343
Estimating Heterogeneous Treatment Effects with Observational Data
Xie, Y., Brand, J. E., & Jann, B. (2012) · Sociological Methodology · doi:10.1177/0081175012452652 · Cited by 330
Best methods for the analysis of change: Recent advances, unanswered questions, future directions.
Collins, L. M., & American Psychological Association (1991) · American Psychological Association eBooks · doi:10.1037/10099-000 · Cited by 337
Reporting structural equation modeling results in Psychology and Aging: Some proposed guidelines.
Raykov, T., Tomer, A., & Nesselroade, J. R. (1991) · Psychology and Aging · doi:10.1037/0882-7974.6.4.499 · Cited by 336
Robins, J. M., Blevins, D., Ritter, G. A., & Wulfsohn, M. S. (1992) · Epidemiology · doi:10.1097/00001648-199207000-00007 · Cited by 328
Design and Analysis of Vaccine Studies
Halloran, M. E., Longini, I. M., & Struchiner, C. J. (2009) · Statistics for biology and health · doi:10.1007/978-0-387-68636-3 · Cited by 332
The Role of General Theory in Comparative-Historical Sociology
Kiser, E. V., & Hechter, M. (1991) · American Journal of Sociology · doi:10.1086/229738 · Cited by 330
Multivariate Regression Analysis for the Item Count Technique
Imai, K. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap10415 · Cited by 326
Elwert, F. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_13 · Cited by 325
Machine Learning for Social Science: An Agnostic Approach
Grimmer, J., Roberts, M. E., & Stewart, B. (2021) · Annual Review of Political Science · doi:10.1146/annurev-polisci-053119-015921 · Cited by 362
The Taboo Against Explicit Causal Inference in Nonexperimental Psychology
Grosz, M. P., Rohrer, J. M., & Thoemmes, F. J. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620921521 · Cited by 358
Theory as method: small theories of treatments
Lipsey, M. W. (1993) · New Directions for Program Evaluation · doi:10.1002/ev.1637 · Cited by 322
Opiates for the Matches: Matching Methods for Causal Inference
Sekhon, J. S. (2009) · Annual Review of Political Science · doi:10.1146/annurev.polisci.11.060606.135444 · Cited by 328
Blinding and expectancy confounds in psychedelic randomized controlled trials
Muthukumaraswamy, S. D., Forsyth, A., & Lumley, T. (2021) · Expert Review of Clinical Pharmacology · doi:10.1080/17512433.2021.1933434 · Cited by 324
Estimating high-dimensional intervention effects from observational data
Maathuis, M. H., Kalisch, M., & Bühlmann, P. (2009) · The Annals of Statistics · doi:10.1214/09-aos685 · Cited by 309
Robust inference on average treatment effects with possibly more covariates than observations
Farrell, M. H. (2015) · Journal of Econometrics · doi:10.1016/j.jeconom.2015.06.017 · Cited by 325
Strategies of Causal Inference in Small-N Analysis
Mahoney, J. (2000) · Sociological Methods & Research · doi:10.1177/0049124100028004001 · Cited by 324
Intention-to-treat analysis and the goals of clinical trials*
Sheiner, L. B., & Rubin, D. B. (1995) · Clinical Pharmacology & Therapeutics · doi:10.1016/0009-9236(95)90260-0 · Cited by 315
Causal Inference in Accounting Research
Gow, I. D., Larcker, D. F., & REISS, P. C. (2016) · Journal of Accounting Research · doi:10.1111/1475-679x.12116 · Cited by 301
Problems in the Measurement of Latent Variables in Structural Equations Causal Models
Cohen, P. R., Cohen, J., Teresi, J. A., Marchi, M., & Vélez, C. N. (1990) · Applied Psychological Measurement · doi:10.1177/014662169001400207 · Cited by 311
From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
Zyphur, M. J., Allison, P. D., Tay, L., Voelkle, M. C., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., et al. (2019) · Organizational Research Methods · doi:10.1177/1094428119847278 · Cited by 342
An overview of relations among causal modelling methods
Greenland, S., & Brumback, B. (2002) · International Journal of Epidemiology · doi:10.1093/ije/31.5.1030 · Cited by 406
Zhang, J. L., & Rubin, D. B. (2003) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986028004353 · Cited by 308
Compliance as an Explanatory Variable in Clinical Trials
Efron, B., & Feldman, D. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10474996 · Cited by 307
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Brady, H. E., & McNulty, J. E. (2011) · American Political Science Review · doi:10.1017/s0003055410000596 · Cited by 307
Statistical Issues in the Study of Temporal Data: Daily Experiences
West, S. G., & Hepworth, J. T. (1991) · Journal of Personality · doi:10.1111/j.1467-6494.1991.tb00261.x · Cited by 307
The Economics and Psychology of Personality Traits
Borghans, L., Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008) · National Bureau of Economic Research · doi:10.3386/w13810 · Cited by 328
Administrative Records Mask Racially Biased Policing
KNOX, D., LOWE, W., & MUMMOLO, J. (2020) · American Political Science Review · doi:10.1017/s0003055420000039 · Cited by 314
Przeworski, A. (2004) · Government and Opposition · doi:10.1111/j.1477-7053.2004.00134.x · Cited by 305
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014) · Oxford Handbooks Online · doi:10.1093/oxfordhb/9780199755615.013.007 · Cited by 305
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Ragin, C. C., & Sonnett, J. (2005) · Vergleichen in der Politikwissenschaft · doi:10.1007/978-3-322-80441-9_9 · Cited by 316
Ecologic versus individual-level sources of bias in ecologic estimates of contextual health effects
Greenland, S. (2001) · International Journal of Epidemiology · doi:10.1093/ije/30.6.1343 · Cited by 304
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Jiang, N., & Li, L. C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03722 · Cited by 303
Heagerty, P. J., & Zeger, S. L. (2000) · Statistical Science · doi:10.1214/ss/1009212671 · Cited by 298
Moving From the Lab to the Field: The Role of Fidelity and Achieved Relative Intervention Strength
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Quasi-experimental and experimental approaches to environmental economics
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Montoya, A. K. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1088-6 · Cited by 331
Imai, K., King, G., & Nall, C. (2009) · Statistical Science · doi:10.1214/08-sts274 · Cited by 293
The Economics and Psychology of Personality Traits
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Salganik, M., & Watts, D. J. (2008) · Social Psychology Quarterly · doi:10.1177/019027250807100404 · Cited by 299
Towards a critical race methodology in algorithmic fairness
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“There has also been significant debate about the appropriate use of racial variables in studies aimed at identifying causal effects [46, 54, 55, 58, 67].”
Behrens, A., Uggen, C., & Manza, J. (2003) · American Journal of Sociology · doi:10.1086/378647 · Cited by 295
Bia, M., & Mattei, A. (2008) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0800800303 · Cited by 286
Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis
Abadie, A., Athey, S. C., Imbens, G. W., & Wooldridge, J. M. (2020) · Econometrica · doi:10.3982/ecta12675 · Cited by 304
Causation in epidemiology: Table 1
Parascandola, M. J., & Weed, D. L. (2001) · Journal of Epidemiology & Community Health · doi:10.1136/jech.55.12.905 · Cited by 294
Recent developments in the econometrics of program evaluation
Wooldridge, J. M., & Imbens, G. W. (2008) · doi:10.1920/wp.cem.2008.2408 · Cited by 323
The Economics and Psychology of Personality TraitsPreprint
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Heinrich, C. J., Maffioli, A., & Vázquez, G. (2010) · doi:10.18235/0008567 · Cited by 280
Gerring, J. (2010) · Comparative Political Studies · doi:10.1177/0010414010376911 · Cited by 279
Murray, J., Farrington, D. P., & Eisner, M. P. (2009) · Journal of Experimental Criminology · doi:10.1007/s11292-008-9066-0 · Cited by 280
Iacobucci, D. (2009) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2009.09.002 · Cited by 279
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Steen, J., Loeys, T., Moerkerke, B., & Vansteelandt, S. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i11 · Cited by 292
Wolfolds, S. E., & Siegel, J. I. (2018) · Strategic Management Journal · doi:10.1002/smj.2995 · Cited by 278
Maciejewski, M. L. (2018) · Biostatistics & Epidemiology · doi:10.1080/24709360.2018.1477468 · Cited by 406
Calcagno, J. C., & Long, B. T. (2008) · National Bureau of Economic Research · doi:10.3386/w14194 · Cited by 274
Latino Immigrants and the U.S. Racial Order
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Fowlie, M., Holland, S. P., & Mansur, E. T. (2012) · American Economic Review · doi:10.1257/aer.102.2.965 · Cited by 315
Generalizing Study Results: A Potential Outcomes Perspective
Lesko, C. R., Buchanan, A. L., Westreich, D., Edwards, J. K., Hudgens, M. G., & Cole, S. R. (2017) · Epidemiology · doi:10.1097/ede.0000000000000664 · Cited by 269
Inflation culture, central bank independence and price stability
Bernd Hayo, A. (1998) · European Journal of Political Economy · doi:10.1016/s0176-2680(98)00006-8 · Cited by 269
Principal Stratification Approach to Broken Randomized Experiments
Barnard, J., Frangakis, C., Hill, J., & Rubin, D. B. (2003) · Journal of the American Statistical Association · doi:10.1198/016214503000071 · Cited by 268
Methodological Wishes for the Next Decade and How to Make Wishes Come True
Aguinis, H., & Edwards, J. R. (2013) · Journal of Management Studies · doi:10.1111/joms.12058 · Cited by 264
Causal inference under multiple versions of treatment
VanderWeele, T. J., & Hernán, M. Á. (2013) · Journal of Causal Inference · doi:10.1515/jci-2012-0002 · Cited by 265
Forced Relocation and Residential Instability among Urban Renters
Desmond, M., Gershenson, C., & Kiviat, B. (2015) · Social Service Review · doi:10.1086/681091 · Cited by 228
Effect Analysis and Causation in Linear Structural Equation Models
Sobel, M. E. (1990) · Psychometrika · doi:10.1007/bf02294763 · Cited by 264
Does knowledge diffusion between university and industry increase innovativeness?
Lööf, H., & Broström, A. (2006) · The Journal of Technology Transfer · doi:10.1007/s10961-006-9001-3 · Cited by 260
Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T., & Busillo, F. (2013) · Papers of the Regional Science Association · doi:10.1111/j.1435-5957.2012.00459.x · Cited by 264
Causal Inference in Infectious Diseases
Halloran, M. E., & Struchiner, C. J. (1995) · Epidemiology · doi:10.1097/00001648-199503000-00010 · Cited by 263
Heckman, J. J. (2008) · International Statistical Review · doi:10.1111/j.1751-5823.2007.00024.x · Cited by 260
Can Policies Stall the Fertility Fall? A Systematic Review of the (Quasi‐) Experimental Literature
Bergsvik, J., Fauske, A., & Hart, R. K. (2021) · Population and Development Review · doi:10.1111/padr.12431 · Cited by 259
Cook, T. D. (2002) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737024003175 · Cited by 256
Hargittai, E., & Shaw, A. (2015) · Information, Communication & Society · doi:10.1080/1369118x.2014.957711 · Cited by 257
Seeking Causal Explanations in Social Epidemiology
Kaufman, J. S., & Cooper, R. (1999) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009969 · Cited by 257
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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