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Statistical Problems in Agricultural Experimentation
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Citations
Cited by
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H., & Wahba, S. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473858 · Cited by 2,395
Causal Inference Using Potential Outcomes: Design, Modeling, Decisions
Rubin, D. B. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001880 · Cited by 2,173
THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA
Winship, C., & Morgan, S. L. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.659 · Cited by 890
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
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376
“…relate to either of the four theoretical milestones: Lewis-Stalnaker’s theory of closest possible worlds [36], [120], Pearl’s Structural Causal Models (SCM) [37], Wood-ward’s Counterfactual Theory of Explanation (CTE) [125], or the Neyman-Rubin Causal Model (NRCM) [107], [115] (see Table 5).”
BackgroundCausal inference with observational data
Nichols, A. (2007) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800700403 · Cited by 339
Average Values of Mean Squares in Factorials
Cornfield, J., & Tukey, J. W. (1956) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728067 · Cited by 292
General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference
Li, X., & Ding, P. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1295865 · Cited by 167
Controversies concerning randomization and additivity in clinical trials.
Senn, S. (2004) · Statistics in Medicine · doi:10.1002/sim.2074 · Cited by 132
Covariate-adjusted Fisher randomization tests for the average treatment effect
Zhao, A., & Ding, P. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2021.04.007 · Cited by 40
Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies
van Smeden, M., Penning de Vries, B. B. L., Nab, L., & Groenwold, R. H. H. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.11.006 · Cited by 32
Metadata
- Authors
- Neyman, J., Iwaszkiewicz, K., & Kołodziejczyk, S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1935-07-01
- Volume
- 2
- Issue
- 2
- Pages
- 107-154
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2803021103
- Cited by
- 485
- References
- 0
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
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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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.