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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 701–800 of 1,019
Hainmueller, J., Hofmann, B., Krug, G., & Wolf, K. (2016) · Scandinavian Journal of Economics · doi:10.1111/sjoe.12166 · Cited by 91
Does Advertising Serve as a Signal? Evidence from a Field Experiment in Mobile Search
Sahni, N. S., & Nair, H. S. (2019) · The Review of Economic Studies · doi:10.1093/restud/rdz053 · Cited by 97
Leviton, A., & Cowan, L. D. (2002) · Food and Chemical Toxicology · doi:10.1016/s0278-6915(02)00092-3 · Cited by 88
Sharp bounds on the causal effects in randomized experiments with “truncation-by-death”
Imai, K. (2007) · Statistics & Probability Letters · doi:10.1016/j.spl.2007.05.015 · Cited by 91
Targeted Maximum Likelihood Estimation of Natural Direct Effects
Zheng, W., & van der Laan, M. J. (2012) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1361 · Cited by 92
Mithas, S., Jones, J. L., & Mitchell, W. (2008) · MIS Quarterly · doi:10.2307/25148868 · Cited by 90
Why you should stop predicting customer churn and start using uplift models
Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90
Causal Inference in the Social Sciences
Imbens, G. W. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-033121-114601 · Cited by 109
Causal Inference With Observational Data and Unobserved Confounding Variables
Byrnes, J. E. K., & Dee, L. E. (2025) · Ecology Letters · doi:10.1111/ele.70023 · Cited by 111
Keele, L. J., & Minozzi, W. (2013) · Political Analysis · doi:10.1093/pan/mps041 · Cited by 86
Toward a reconceptualization of the law of initial value.
Jin, P. (1992) · Psychological Bulletin · doi:10.1037/0033-2909.111.1.176 · Cited by 88
Interpretation of Cointegrating Coefficients in the Cointegrated Vector Autoregressive Model*
Johansen, S. (2005) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2005.00111.x · Cited by 89
Cause, effect and regression in road safety: A case study
Hauer, E. (2010) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.12.027 · Cited by 89
Frumento, P., Mealli, F., Pacini, B., & Rubin, D. B. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2011.643719 · Cited by 89
Pitfalls of Multisite Randomized Clinical Trials of Efficacy and Effectiveness
KRAEMER, H. C. (2000) · Schizophrenia Bulletin · doi:10.1093/oxfordjournals.schbul.a033474 · Cited by 89
Learning probabilistic networks
Krause, P. J. (1999) · The Knowledge Engineering Review · doi:10.1017/s0269888998004019 · Cited by 89
Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)
Imbens, G. W. (2009) · National Bureau of Economic Research · doi:10.3386/w14896 · Cited by 89
Senn, S. J., Graf, E., & Caputo, A. (2007) · Statistics in Medicine · doi:10.1002/sim.3133 · Cited by 87
Wang, C., Hu, J., Blaser, M. J., & Li, H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz565 · Cited by 91
Galles, D., & Pearl, J. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00047-7 · Cited by 84
Glymour, M. M., Weuve, J., & Chen, J. T. (2008) · Neuropsychology Review · doi:10.1007/s11065-008-9066-x · Cited by 87
Imai, K. (2008) · Statistics in Medicine · doi:10.1002/sim.3337 · Cited by 88
The Microeconometric Estimation of Treatment Effects — An Overview
Caliendo, M., & Hujer, R. (2007) · Springer eBooks · doi:10.1007/3-540-32693-6_14 · Cited by 88
Gubernatorial Effects on the Voting Behavior of National Legislators
Rosas, G., & Langston, J. (2011) · The Journal of Politics · doi:10.1017/s0022381611000326 · Cited by 87
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
Khademi, A., Lee, S., Foley, D., & Honavar, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313559 · Cited by 95
Inferring ecosystem networks as information flows
Li, J., & Convertino, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-86476-9 · Cited by 88
Pattanayak, C. W., Rubin, D. B., & Zell, E. R. (2011) · Revista Española de Cardiología · doi:10.1016/j.recesp.2011.06.008 · Cited by 90
Dietrich, A. (2011) · Empirical Economics · doi:10.1007/s00181-011-0510-z · Cited by 87
Gustafsson, J. (2013) · School Effectiveness and School Improvement · doi:10.1080/09243453.2013.806334 · Cited by 84
For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates
Greenland, S. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0230-6 · Cited by 86
A Spatial Perspective on the Econometrics of Program Evaluation
Kolak, M., & Anselin, L. (2019) · International Regional Science Review · doi:10.1177/0160017619869781 · Cited by 86
EDA for HLM: Visualization when Probabilistic Inference Fails
Bowers, J., & Drake, K. W. (2005) · Political Analysis · doi:10.1093/pan/mpi031 · Cited by 86
Looking Back on “Causal Thinking in the Health Sciences”
Kaufman, J. S., & Poole, C. (2000) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.21.1.101 · Cited by 88
Non-parametric regression for binary dependent variables
Frölich, M. (2006) · Econometrics Journal · doi:10.1111/j.1368-423x.2006.00196.x · Cited by 86
The SAGE International Handbook of Educational Evaluation
Ryan, K. E., & Cousins, J. (2009) · Sage eBooks · doi:10.4135/9781452226606 · Cited by 87
The Assumptions on Which Causal Inferences Rest
Stone, R. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01915.x · Cited by 86
Believability of relative risks and odds ratios in abstracts: Cross sectional study.
Gøtzsche, P. C. (2006) · BMJ · doi:10.1136/bmj.38895.410451.79 · Cited by 85
No Slack: The Financial Lives of Low-Income Americans
Barr, M. S. (2012) · Project Muse (Johns Hopkins University) · Cited by 84
Thambirajah, J., Benabbas, L., Bauer, M., & Thornhill, N. F. (2008) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.10.002 · Cited by 85
Mark, S. D., & Robins, J. M. (1993) · Statistics in Medicine · doi:10.1002/sim.4780121707 · Cited by 83
Panel Data Analysis in Public Administration: Substantive and Statistical Considerations
Zhu, L. (2012) · Journal of Public Administration Research and Theory · doi:10.1093/jopart/mus064 · Cited by 85
Treatment Effect Heterogeneity in Theory and PracticePreprint
Angrist, J. D. (2003) · SSRN Electronic Journal · doi:10.2139/ssrn.432501 · Cited by 85
Analysis of randomised trials with long-term follow-up
Herbert, R. D., Kasza, J., & Bø, K. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0499-5 · Cited by 80
Drawing Your Senator from a Jar:Term Length and Legislative Behavior
Titiunik, R. (2015) · Political Science Research and Methods · doi:10.1017/psrm.2015.20 · Cited by 83
Calcagno, J. C., & Long, B. T. (2008) · Cited by 84
Sasidharan, L., & Donnell, E. T. (2012) · Accident Analysis & Prevention · doi:10.1016/j.aap.2012.05.036 · Cited by 84
The consequences of unobserved heterogeneity in a sequential logit model
Buis, M. L. (2010) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2010.12.006 · Cited by 87
Blossfeld, H. P. (1996) · European Sociological Review · doi:10.1093/oxfordjournals.esr.a018185 · Cited by 84
CAUSALITY AND PRICE DISCOVERY: AN APPLICATION OF DIRECTED ACYCLIC GRAPHS
Haigh, M. S., & Bessler, D. A. (2002) · AgEcon Search (University of Minnesota, USA) · doi:10.22004/ag.econ.19057 · Cited by 84
Social and environmental impacts of agricultural cooperatives: evidence from Ethiopia
Mojo, D., Fischer, C., & Degefa, T. (2015) · International Journal of Sustainable Development & World Ecology · doi:10.1080/13504509.2015.1052860 · Cited by 91
Targeted Learning of the Mean Outcome under an Optimal Dynamic Treatment Rule
van der Laan, M. J., & Luedtke, A. R. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0022 · Cited by 91
Gøtzsche‐Astrup, O. (2018) · Aggression and Violent Behavior · doi:10.1016/j.avb.2018.02.003 · Cited by 85
Newman, M. G., & Fisher, A. J. (2010) · International Journal of Cognitive Therapy · doi:10.1521/ijct.2010.3.3.245 · Cited by 83
Husain, M., & Millimet, D. L. (2008) · Economics of Education Review · doi:10.1016/j.econedurev.2007.11.002 · Cited by 83
Beyond intent to treat (ITT): A complier average causal effect (CACE) estimation primer
Peugh, J., Strotman, D., McGrady, M. E., Rausch, J. R., & Kashikar‐Zuck, S. (2016) · Journal of School Psychology · doi:10.1016/j.jsp.2015.12.006 · Cited by 85
Causal Diagrams: Pitfalls and Tips
Suzuki, E., Shinozaki, T., & Yamamoto, E. (2020) · Journal of Epidemiology · doi:10.2188/jea.je20190192 · Cited by 61
Rethinking causal explanation in interpretive international studies
Norman, L. (2021) · European Journal of International Relations · doi:10.1177/13540661211006454 · Cited by 92
Prompts, Not Questions: Four Techniques for Crafting Better Interview Protocols
Jiménez, T. R., & Orozco, M. (2021) · Qualitative Sociology · doi:10.1007/s11133-021-09483-2 · Cited by 83
Causal Inference in Environmental Impact Studies
Beyers, D. W. (1998) · Journal of the North American Benthological Society · doi:10.2307/1468339 · Cited by 83
Self-employment and attitudes towards risk: Timing and unobserved heterogeneity
Brown, S., Dietrich, M. R., Ortiz‐Nuñez, A., & Taylor, K. B. (2011) · Journal of Economic Psychology · doi:10.1016/j.joep.2011.02.015 · Cited by 83
Causal Inference in Sociological Studies
Winship, C., & Sobel, M. E. (2004) · SAGE Publications Ltd eBooks · doi:10.4135/9781848608184.n21 · Cited by 81
Causal inference and data fusion in econometrics
Hünermund, P., & Bareinboim, E. (2023) · Econometrics Journal · doi:10.1093/ectj/utad008 · Cited by 82
Can Wrongful Discharge Law Enhance Employment?
MacLeod, W. B., & Nakavachara, V. (2007) · The Economic Journal · doi:10.1111/j.1468-0297.2007.02056.x · Cited by 82
Kaufman, J. S. (2008) · Social Science & Medicine · doi:10.1016/j.socscimed.2007.11.046 · Cited by 87
Graphical Models for Causation, and the Identification Problem
Freedman, D. A. (2004) · Evaluation Review · doi:10.1177/0193841x04266432 · Cited by 83
Causal Inference from 2K Factorial Designs by Using Potential Outcomes
Dasgupta, T., Pillai, N. S., & Rubin, D. B. (2014) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12085 · Cited by 87
Frederiks, E. R., Stenner, K., Hobman, E. V., & Fischle, M. (2016) · Energy Research & Social Science · doi:10.1016/j.erss.2016.08.020 · Cited by 90
Who Matches? Propensity Scores and Bias in the Causal Effects of Education on Participation
Henderson, J. A., & Chatfield, S. (2011) · The Journal of Politics · doi:10.1017/s0022381611000351 · Cited by 82
Schooling, Statistics, and Poverty: Can We Measure School Improvement?.
Raudenbush, S. W. (2004) · TOEFL iBT research reports · Cited by 82
Haas, S. K., Kreutz, R. H., Mantovani, L. G., Pattanayak, C. W., Holberg, G., Jamal, W., Schmidt, A., van Eickels, M., Lassen, M. R., & Turpie, A. G. G. (2013) · Thrombosis and Haemostasis · doi:10.1160/th13-08-0666 · Cited by 82
Generating Usable Knowledge through an Experimental Approach to Public Administration
Jilke, S., de Walle, S. V., & Kim, S. (2015) · Public Administration Review · doi:10.1111/puar.12488 · Cited by 81
Beyond Risk Factors: An Analytical Approach to Crime Prevention
Wikström, P. H., & Treiber, K. (2016) · Springer eBooks · doi:10.1007/978-3-319-44124-5_8 · Cited by 82
Intersectional decomposition analysis with differential exposure, effects, and construct
Jackson, J. W., & VanderWeele, T. J. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.01.033 · Cited by 86
Imbens, G. W., & Rubin, D. B. (2010) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230280816_28 · Cited by 85
Ensemble methods for uplift modeling
Sołtys, M., Jaroszewicz, S., & Rzepakowski, P. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0383-9 · Cited by 81
Causal Inference for a Population of Causally Connected Units
van der Laan, M. J. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0002 · Cited by 78
Formation Stories and Causality in Sociology
Hirschman, D., & Reed, I. A. (2014) · Sociological Theory · doi:10.1177/0735275114558632 · Cited by 87
CAUSAL INFERENCE ON EDUCATION POLICIES: A SURVEY OF EMPIRICAL STUDIES USING PISA, TIMSS AND PIRLS
Ferrera, J. M. C., Cristóbal, V., & González, D. S. (2017) · Journal of Economic Surveys · doi:10.1111/joes.12217 · Cited by 93
Public opinion on nuclear energy and nuclear weapons: The attitudinal nexus in the United States
Baron, J., & Herzog, S. (2020) · Energy Research & Social Science · doi:10.1016/j.erss.2020.101567 · Cited by 80
Are There Algorithms That Discover Causal Structure?
Freedman, D. A., & Humphreys, P. W. (1999) · Synthese · doi:10.1023/a:1005277613752 · Cited by 79
Regression analysis with clustered data
Graubard, B. I., & Korn, E. L. (1994) · Statistics in Medicine · doi:10.1002/sim.4780130514 · Cited by 80
Incomes in South Africa Since the Fall of Apartheid
Leibbrandt, M., Levinsohn, J., & McCrary, J. (2005) · National Bureau of Economic Research · doi:10.3386/w11384 · Cited by 80
Causal Modeling in Environmental Health
Bind, M. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040218-044048 · Cited by 82
Hoogland, J., IntHout, J., Belias, M., Rovers, M. M., Riley, R. D., Harrell, F. E., Moons, K. G. M., Debray, T. P. A., & Reitsma, J. B. (2021) · Statistics in Medicine · doi:10.1002/sim.9154 · Cited by 79
Rijnhart, J. J. M., Valente, M. J., Smyth, H. L., & MacKinnon, D. P. (2021) · Prevention Science · doi:10.1007/s11121-021-01308-6 · Cited by 82
Bayesian causal inference: a critical review
Li, F., Ding, P., & Mealli, F. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0153 · Cited by 83
Does the EUcausedomestic developments? improving case selection in Europeanisation research
Haverland, M. (2005) · West European Politics · doi:10.1080/01402380500389331 · Cited by 78
Clarke, K. A. (2009) · Conflict Management and Peace Science · doi:10.1177/0738894208097666 · Cited by 81
Gerring, J. (2006) · International Sociology · doi:10.1177/0268580906067837 · Cited by 79
Estimating Causal Effects Using School-Level Data Sets
Stuart, E. A. (2007) · Educational Researcher · doi:10.3102/0013189x07303396 · Cited by 80
Methodology in Our Education Research Culture
Henson, R. K., Hull, D. M., & Williams, C. S. (2010) · Educational Researcher · doi:10.3102/0013189x10365102 · Cited by 79
Counterfactual Estimation and Optimization of Click Metrics in Search Engines
Li, L., Chen, S., Kleban, J., & Gupta, A. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2740908.2742562 · Cited by 82
A causal perspective on dataset bias in machine learning for medical imaging
Jones, C., Castro, D. C., Ribeiro, F. D. S., Oktay, O., McCradden, M. D., & Glocker, B. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00797-8 · Cited by 110
Bayesian analysis of cross-section and clustered data treatment models
Chib, S., & Hamilton, B. H. (2000) · Journal of Econometrics · doi:10.1016/s0304-4076(99)00065-2 · Cited by 78
Accident reduction factors and causal inference in traffic safety studies: a review
Davis, G. A. (2000) · Accident Analysis & Prevention · doi:10.1016/s0001-4575(99)00050-0 · Cited by 78
Empirical research methods for technology validation: Scaling up to practice
Wieringa, R. J. (2013) · Journal of Systems and Software · doi:10.1016/j.jss.2013.11.1097 · Cited by 78
INFERENCE PROBLEMS WITH HIERARCHICAL MULTIPLE REGRESSION-BASED TESTS OF MEDIATING EFFECTS
Stone‐Romero, E. F., & Rosopa, P. J. (2004) · Research in personnel and human resources management · doi:10.1016/s0742-7301(04)23006-7 · Cited by 78
The map comparison problem: tests for the overlap of geographic boundaries.
Jacquez, G. (1995) · Statistics in Medicine · doi:10.1002/sim.4780142107 · Cited by 82
Confounding-Robust Policy ImprovementPreprint
Kallus, N., & Zhou, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08593 · Cited by 78
Intrinsic Motivation in Public Service: Theory and Evidence from State Supreme Courts
Ash, E., & MacLeod, W. B. (2015) · The Journal of Law and Economics · doi:10.1086/684293 · Cited by 74
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
