The DOI matches its Crossref record; 18 of the 20 references it cites were found in a registry or an index.
A New Approach To Causal Inference in Mortality Studies With a Sustained Exposure Period - Application To Control of the Healthy Worker Survivor Effect
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The cited source says what the claim attributes to it.
In the citing paper
“the biostatistics literature has focused on sequential random ignorability assumptions that impose that treatment in each period is random conditional on the path of covariates and realized outcomes, rather than parallel trends.”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Robins (1986)
The cited source says what the claim attributes to it.
In the citing paper
“Note also that the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time varying treatment with time varying confounders (Robins, 1986; Arjas and Parner, 2004).”
From Causal inference in statistics: An overview (2009) · cites this paper as Robins, 1986
The cited source says what the claim attributes to it.
In the citing paper
“Note also that the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time varying treatment with time varying confounders (Robins, 1986, Arjas andParner, 2004).”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
The cited source says what the claim attributes to it.
In the citing paper
“the algebraic machinery offered by the counterfactual notation, Y x (u), once a problem is properly formalized, can be extremely powerful in refining assumptions (Angrist et al., 1996, Heckman andVytlacil, 2005), deriving consistent estimands (Robins, 1986), bounding probabilities of necessary and sufficient causation (Tian and Pearl, 2000), and combining data from experimental and nonexperimental”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“They are assumed to be connected to observed variables via consistency constraints (Robins, 1986) such as X = x =⇒ Y x = Y,(32)”
From An Introduction to Causal Inference (2010) · cites this paper as Robins, 1986
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Equitable vaccination coverage requires higher levels of coverage among those most at risk at the intersection of SARS-CoV-2 exposures, severity, and onward transmission(44-48).”
From Social inequalities in COVID-19 death by area-level income in 11.2 million people in Ontario, Canada: patterns over time and the mediating role of vaccination. (2026) · cites this paper as [44]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
The Statistical Analysis of Failure Time Data.
Aitkin, M. A., Kalbfleisch, J. D., & Prentice, R. L. (1981) · Biometrics · doi:10.2307/2530581 · Cited by 9,875
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
Conditional Independence in Statistical Theory
Dawid, A. P. (1979) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1979.tb01052.x · Cited by 1,507
Information and Exponential Families in Statistical Theory.
Kingmán, J. F. C., & Barndorff-Nielsen, O. E. (1979) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344658 · Cited by 1,188
The Consquences of Adjustment for a Concomitant Variable That Has Been Affected by the Treatment
Rosenbaum, P. R. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981697 · Cited by 849
Multiplicative Models and Cohort Analysis
Breslow, N. E., Lubin, J. H., Marek, P. M., & Langholz, B. M. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477915 · Cited by 566
CONFOUNDING: ESSENCE AND DETECTION1
Miettinen, O. S., & Cook, E. F. (1981) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113225 · Cited by 350
Arsenic and Respiratory Cancer in Man: An Occupational Study
Lee, A. M., & Fraumeni, J. F. (1969) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/42.6.1045 · Cited by 253
General Relative-Risk Models for Survival Time and Matched Case-Control Analysis
Thomas, D. C. (1981) · Biometrics · doi:10.2307/2530149 · Cited by 183
Research works printed but not linked: 5
- Robins, 1984, A statistical method to control for the healthy worker effect in intracohort comparisons, Am. J. Epidemiology, 120, 465
- Robins, 1985, A new theory of causality in observational survival studies—Application to the healthy worker effect, Biometrics, 41, 331
- Robins, 1986, Some theorems relating to causal tree graphs
- Thomas, 1977, Methods of cohort analysis. Appraisal by application to asbestos mining (with discussion), J. R. Statist. Soc., A140, 469
- Breslow, 1972, Contribution to discussion of paper by D.R. Cox, J. R. Statist. Soc., B34, 216
Cited by
Sorted by citations
Difference-in-Differences with multiple time periods
Callaway, B., & Sant’Anna, P. H. C. (2021) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.12.001 · Cited by 9,126
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Self-citationCausal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Making things happen: a theory of causal explanation
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5840 · Cited by 3,383
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115
Self-citationA structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004) · Epidemiology · doi:10.1097/01.ede.0000135174.63482.43 · Cited by 2,844
Self-citationCausal inference in statistics: An overview
Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336
“Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994; Greenland et al., 1999; Cole and Hernán, 2002; Arah, 2008; Shrier, 2009; Pearl, 2009b).”
BackgroundInfluentialModeling and variable selection in epidemiologic analysis
Greenland, S. (1989) · American Journal of Public Health · doi:10.2105/ajph.79.3.340 · Cited by 2,208
What's trending in difference-in-differences? A synthesis of the recent econometrics literature
Roth, J., Sant’Anna, P. H. C., Bilinski, A., & Poe, J. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2023.03.008 · Cited by 2,027
“A similar setting has been studied extensively in biostatistics, beginning with the pioneering work of Robins (1986).”
BackgroundMethodInfluentialDoubly robust estimation in missing data and causal inference models
Bang, H., & Robins, J. M. (2005) · Biometrics · doi:10.1111/j.1541-0420.2005.00377.x · Cited by 1,998
Self-citationIdentifiability and exchangeability for direct and indirect effects
Robins, J. M., & Greenland, S. (1992) · Epidemiology · doi:10.1097/00001648-199203000-00013 · Cited by 1,793
Self-citationA Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures
Keil, A. P., Buckley, J. P., O’Brien, K. M., Ferguson, K. K., Zhao, S., & White, A. J. (2020) · Environmental Health Perspectives · doi:10.1289/ehp5838 · Cited by 1,787
A review of instrumental variable estimators for Mendelian randomization
Burgess, S., Small, D. S., & Thompson, S. G. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280215597579 · Cited by 1,769
A Tutorial on Learning with Bayesian Networks
Heckerman, D. E. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-85066-3_3 · Cited by 1,712
Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
Robins, J. M., Rotnitzky, A. G., & Zhao, L. P. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476493 · Cited by 1,597
Self-citationPrincipal Stratification in Causal Inference
Frangakis, C., & Rubin, D. B. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00021.x · Cited by 1,543
Causal Inference in Statistics
Pearl, J., Glymour, M., & Jewell, N. P. (2016) · Cited by 1,436
A Tutorial on Learning with Bayesian Networks
Heckerman, D. E. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9_11 · Cited by 1,242
Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable
Elwert, F., & Winship, C. (2014) · Annual Review of Sociology · doi:10.1146/annurev-soc-071913-043455 · Cited by 1,168
Doubly robust estimation of causal effects
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq439 · Cited by 1,158
Hernán, M. A., Sauer, B. C., Hernández-Díaz, S., Platt, R., & Shrier, I. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.04.014 · Cited by 1,097
Estimating causal effects from epidemiological data
Hernán, M. Á., & Robins, J. M. (2006) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2004.029496 · Cited by 1,029
Self-citationAdjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., Rotnitzky, A. G., & Robins, J. M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10473862 · Cited by 1,049
Self-citationOptimal Dynamic Treatment Regimes
Murphy, S. A. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00389 · Cited by 990
An introduction to inverse probability of treatment weighting in observational research
Chesnaye, N. C., Stel, V. S., Tripepi, G. L., Dekker, F. W., Fu, E. L., Zoccali, C., & Jager, K. J. (2021) · Clinical Kidney Journal · doi:10.1093/ckj/sfab158 · Cited by 983
Instruments for Causal Inference: An Epidemiologist's Dream?
Hernán, M. A., & Robins, J. M. (2006) · Epidemiology · doi:10.1097/01.ede.0000222409.00878.37 · Cited by 961
Self-citationConfounding and Collapsibility in Causal Inference
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Statistical Science · doi:10.1214/ss/1009211805 · Cited by 933
Self-citationTHE 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
Comparative quantification of health risks conceptual framework and methodological issues
Murray, C. J., Ezzati, M., Lopez, A. D., Rodgers, A., & Vander Hoorn, S. (2003) · Population Health Metrics · doi:10.1186/1478-7954-1-1 · Cited by 887
Robins, J. M., & Finkelstein, D. M. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00779.x · Cited by 877
Self-citationObservational Studies Analyzed Like Randomized Experiments
Hernán, M. Á., Alonso, Á., Logan, R. W., Grodstein, F., Michels, K. B., Willett, W. C., Manson, J. E., & Robins, J. M. (2008) · Epidemiology · doi:10.1097/ede.0b013e3181875e61 · Cited by 849
Self-citationAdjusted survival curves with inverse probability weights
Cole, S. R., & Hernán, M. Á. (2003) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2003.10.004 · Cited by 836
Mediation analysis in epidemiology: methods, interpretation and bias
Richiardi, L., Bellocco, R., & Zugna, D. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt127 · Cited by 822
An Introduction to Causal Inference
Pearl, J. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1203 · Cited by 755
Difference-in-Differences Estimators of Intertemporal Treatment Effects
de Chaisemartin, C., & D’Haultfœuille, X. (2026) · Review of Economics and Statistics · doi:10.1162/rest_a_01414 · Cited by 740
An experimental design for the development of adaptive treatment strategies
Murphy, S. A. (2005) · Statistics in Medicine · doi:10.1002/sim.2022 · Cited by 746
DOES MARRIAGE REDUCE CRIME? A COUNTERFACTUAL APPROACH TO WITHIN‐INDIVIDUAL CAUSAL EFFECTS*
Sampson, R. J., Laub, J. H., & Wimer, C. (2006) · Criminology · doi:10.1111/j.1745-9125.2006.00055.x · Cited by 683
Eight Myths About Causality and Structural Equation Models
Bollen, K. A., & Pearl, J. (2013) · Handbooks of sociology and social research · doi:10.1007/978-94-007-6094-3_15 · Cited by 680
Causal Inference by using Invariant Prediction: Identification and Confidence Intervals
Peters, J., Bühlmann, P., & Meinshausen, N. (2016) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12167 · Cited by 112
Per-Protocol Analyses of Pragmatic Trials
Hernán, M. A., & Robins, J. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmsm1605385 · Cited by 660
Self-citationA Unification of Mediation and Interaction
VanderWeele, T. J. (2014) · Epidemiology · doi:10.1097/ede.0000000000000121 · Cited by 681
Bayesian Networks for Data Mining
Heckerman, D. E. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009730122752 · Cited by 631
A Conceptual Framework for Adaptive Preventive Interventions
Collins, L. M., Murphy, S. A., & Bierman, K. L. (2004) · Prevention Science · doi:10.1023/b:prev.0000037641.26017.00 · Cited by 608
Marginal Structural Models for the Estimation of Direct and Indirect Effects
VanderWeele, T. J. (2009) · Epidemiology · doi:10.1097/ede.0b013e31818f69ce · Cited by 583
Neighborhood Effects in Temporal Perspective
Wodtke, G., Harding, D. J., & Elwert, F. (2011) · American Sociological Review · doi:10.1177/0003122411420816 · Cited by 474
Marginal Structural Models versus Structural nested Models as Tools for Causal inference
Robins, J. M. (2000) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4612-1284-3_2 · Cited by 557
Self-citationA "SMART" Design for Building Individualized Treatment Sequences
Lei, H., Nahum-Shani, I., Lynch, K., Oslin, D., & Murphy, S. A. (2012) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032511-143152 · Cited by 557
The Evolving Concept of the Healthy Worker Survivor Effect
Arrighi, H. M., & Hertz‐Picciotto, I. (1994) · Epidemiology · doi:10.1097/00001648-199403000-00009 · Cited by 553
Diagnosing and responding to violations in the positivity assumption
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548
Fallibility in estimating direct effects
Cole, S. R., & Hernán, M. A. (2002) · International Journal of Epidemiology · doi:10.1093/ije/31.1.163 · Cited by 548
Optimal Structural Nested Models for Optimal Sequential Decisions
Robins, J. M. (2004) · Lecture notes in statistics · doi:10.1007/978-1-4419-9076-1_11 · Cited by 547
Self-citationCorrecting for non-compliance in randomized trials using structural nested mean models
Robins, J. M. (1994) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929408831393 · Cited by 534
Self-citationExplaining Causal Findings Without Bias: Detecting and Assessing Direct Effects
Acharya, A. R., Blackwell, M., & Sen, M. (2016) · American Political Science Review · doi:10.1017/s0003055416000216 · Cited by 542
Snowden, J. M., Rose, S., & Mortimer, K. M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq472 · Cited by 514
Causal Inference from Complex Longitudinal Data
Robins, J. M. (1997) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-1842-5_4 · Cited by 510
Self-citationQ-Learning: Theory and Applications
Clifton, J., & Laber, E. B. (2020) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-031219-041220 · Cited by 527
Tocilizumab for Treatment of Mechanically Ventilated Patients With COVID-19
Somers, E. C., Eschenauer, G. A., Troost, J. P., Golob, J. L., Gandhi, T. N., Wang, L., Zhou, N., Petty, L. A., Baang, J. H., Dillman, N. O., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa954 · Cited by 505
Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
Hernán, M. Á., Brumback, B., & Robins, J. M. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753168154 · Cited by 483
Self-citationFitzmaurice, G. M., Davidian, M., Molenberghs, G., & Molenberghs, G. (2008) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420011579 · Cited by 474
Selection Bias Due to Loss to Follow Up in Cohort Studies
Howe, C. J., Cole, S. R., Lau, B. M., Napravnik, S., & Eron, J. J. (2015) · Epidemiology · doi:10.1097/ede.0000000000000409 · Cited by 471
Bootstrap inference when using multiple imputation
Schomaker, M., & Heumann, C. (2018) · Statistics in Medicine · doi:10.1002/sim.7654 · Cited by 495
Statistics in Epidemiology: The Case-Control Study
Breslow, N. E. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476660 · Cited by 459
External Validity: From Do-Calculus to Transportability Across Populations
Pearl, J., & Bareinboim, E. (2014) · Statistical Science · doi:10.1214/14-sts486 · Cited by 455
A definition of causal effect for epidemiological research
Hernán, M. Á. (2004) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2002.006361 · Cited by 455
Counterfactual VQA: A Cause-Effect Look at Language Bias
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X., & Wen, J.-R. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01251 · Cited by 464
Bias Formulas for Sensitivity Analysis for Direct and Indirect Effects
VanderWeele, T. J. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181df191c · Cited by 435
CMAverse: A Suite of Functions for Reproducible Causal Mediation Analyses
Shi, B., Choirat, C., Coull, B. A., VanderWeele, T. J., & Valeri, L. (2021) · Epidemiology · doi:10.1097/ede.0000000000001378 · Cited by 413
The Legacy of Disadvantage: Multigenerational Neighborhood Effects on Cognitive Ability
Sharkey, P. T., & Elwert, F. (2011) · American Journal of Sociology · doi:10.1086/660009 · Cited by 425
Experimental Designs for Identifying Causal Mechanisms
Imai, K., Tingley, D., & Yamamoto, T. (2013) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2012.01032.x · Cited by 408
Association, Causation, And Marginal Structural Models
Robins, J. M. (1999) · Synthese · doi:10.1023/a:1005285815569 · Cited by 404
Self-citationCausal Inference Without Counterfactuals
Dawid, A. P. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474210 · Cited by 414
Causal Inference in Public Health
Glass, T. A., Goodman, S. N., Hernán, M. Á., & Samet, J. M. (2013) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031811-124606 · Cited by 422
Effect Decomposition in the Presence of an Exposure-Induced Mediator-Outcome Confounder
VanderWeele, T. J., Vansteelandt, S., & Robins, J. M. (2014) · Epidemiology · doi:10.1097/ede.0000000000000034 · Cited by 420
Self-citationTargeted Maximum Likelihood Estimation for Causal Inference in Observational Studies
Schuler, M. S., & Rose, S. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kww165 · Cited by 421
Marginal Mean Models for Dynamic Regimes
Murphy, S. A., van der Laan, M. J., Robins, J. M., & Conduct Problems Prevention Research Group (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382327 · Cited by 393
Self-citationConfounding in Health Research
Greenland, S., & Morgenstern, H. (2001) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.22.1.189 · Cited by 390
Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
Robins, J. M., Mark, S. D., & Newey, W. K. (1992) · Biometrics · doi:10.2307/2532304 · Cited by 384
Self-citationImai, K., & Yamamoto, T. (2013) · Political Analysis · doi:10.1093/pan/mps040 · Cited by 395
Methods for dealing with time‐dependent confounding
Daniel, R., Cousens, S. N., Stavola, B. L. D., Kenward, M. G., & Sterne, J. A. C. (2012) · Statistics in Medicine · doi:10.1002/sim.5686 · Cited by 378
An Introduction to the Augmented Inverse Propensity Weighted Estimator
Glynn, A. N., & Quinn, K. M. (2010) · Political Analysis · doi:10.1093/pan/mpp036 · Cited by 374
Robins, J. M., Rotnitzky, A. G., & Scharfstein, D. O. (2000) · The IMA volumes in mathematics and its applications · doi:10.1007/978-1-4612-1284-3_1 · Cited by 371
Self-citationLONG-RUN EFFECTS OF PUBLIC SECTOR SPONSORED TRAINING IN WEST GERMANY
Lechner, M., Miquel, R., & Wunsch, C. (2011) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2011.01029.x · Cited by 362
Emsley, R., Dunn, G., & White, I. R. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280209105014 · Cited by 356
Bias in occupational epidemiology studies
Pearce, N. E., Checkoway, H., & Kriebel, D. (2006) · Occupational and Environmental Medicine · doi:10.1136/oem.2006.026690 · Cited by 350
Healthy worker effect phenomenon: Revisited with emphasis on statistical methods – A review
Chowdhury, R., Shah, D., & Payal, A. R. (2017) · Indian Journal of Occupational and Environmental Medicine · doi:10.4103/ijoem.ijoem_53_16 · Cited by 347
Causal Inference without Counterfactuals
Dawid, A. P. (2001) · Applied logic series · doi:10.1007/978-94-017-1586-7_3 · Cited by 343
Graphs, Causality, and Structural Equation Models
PEARL, J. (1998) · Sociological Methods & Research · doi:10.1177/0049124198027002004 · Cited by 341
Intervening on risk factors for coronary heart disease: an application of the parametric g-formula.
Taubman, S., Robins, J., Mittleman, M., & Hernán, M. (2009) · International Journal of Epidemiology · doi:10.1093/ije/dyp192 · Cited by 359
Self-citationHernán, M. Á., Brumback, B., & Robins, J. M. (2002) · Statistics in Medicine · doi:10.1002/sim.1144 · Cited by 335
Self-citationYoung, J. G., Stensrud, M. J., Tchetgen, E. J. T., & Hernán, M. Á. (2020) · Statistics in Medicine · doi:10.1002/sim.8471 · Cited by 335
Estimation of the causal effects of time-varying exposures
Robins, J. M., & Hernán, M. Á. (2008) · Chapman & Hall/CRC interdisciplinary statistics series/Interdisciplinary statistics · doi:10.1201/9781420011579.ch23 · Cited by 330
Self-citationRobins, J. M., Blevins, D., Ritter, G. A., & Wulfsohn, M. S. (1992) · Epidemiology · doi:10.1097/00001648-199207000-00007 · Cited by 328
Self-citationStatistical methods for Mendelian randomization in genome-wide association studies: A review
Boehm, F. J., & Zhou, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.015 · Cited by 333
Elwert, F. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_13 · Cited by 325
Causal inference and longitudinal data: a case study of religion and mental health
VanderWeele, T. J., Jackson, J. W., & Li, S. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1281-9 · Cited by 322
Causal Mediation Analysis with Multiple Mediators
Daniel, R., Stavola, B. L. D., Cousens, S., & Vansteelandt, S. (2014) · Biometrics · doi:10.1111/biom.12248 · Cited by 338
Robins, J. (1987) · Journal of Chronic Diseases · doi:10.1016/s0021-9681(87)80018-8 · Cited by 305
Self-citationAdvancing functional connectivity research from association to causation
Reid, A., Headley, D. B., Mill, R. D., Sánchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0510-4 · Cited by 303
Experimental design and primary data analysis methods for comparing adaptive interventions.
Nahum-Shani, I., Qian, M., Almirall, D., Pelham, W. E., Gnagy, B., Fabiano, G. A., Waxmonsky, J. G., Yu, J., & Murphy, S. A. (2012) · Psychological Methods · doi:10.1037/a0029372 · Cited by 298
Methods of Public Health Research — Strengthening Causal Inference from Observational Data
Hernán, M. Á. (2021) · New England Journal of Medicine · doi:10.1056/nejmp2113319 · Cited by 298
Metadata
- Authors
- Robins, J.
- Affiliations
- Harvard University (US)
- Corresponding author
- Robins, James
- ORCID
- Robins, James
- Venue
- Mathematical Modelling
- Publisher
- Elsevier BV
- Published
- 1986
- Volume
- 7
- Issue
- 9-12
- Pages
- 1393-1512
- ISSN
- 0270-0255 (ISSN-L)
- OpenAlex
- W2082299845
- Cited by
- 2,451
- References
- 20
- Citations by year
- 2026: 148 · 2025: 177 · 2024: 221 · 2023: 167 · 2022: 182 · 2021: 167 · 2020: 170 · 2019: 137 · 2018: 107 · 2017: 104 · 2016: 99 · 2015: 98 · 2014: 100 · 2013: 67 · 2012: 85
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Mathematical Modelling (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- causal inference, mortality studies, healthy worker survivor effect, occupational mortality, arsenic exposure, copper smelter workers, lung cancer mortality, all-cause mortality, causal graphical models, mortality risk factors
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
