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
Items 201–300 of 1,004
Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden VariablesPreprint
Pearl, J., & Robins, J. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4977 · Cited by 133
Self-citationUniform consistency in causal inference
Robins, J. M. (2003) · Biometrika · doi:10.1093/biomet/90.3.491 · Cited by 132
Self-citationThe Book of Why: The New Science of Cause and Effect
Aronow, P. M., & Sävje, F. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1721245 · Cited by 132
Ferguson, K. K., Chen, Y., VanderWeele, T. J., McElrath, T. F., Meeker, J. D., & Mukherjee, B. (2016) · Environmental Health Perspectives · doi:10.1289/ehp282 · Cited by 131
Nonparametric Causal Effects Based on Incremental Propensity Score Interventions
Kennedy, E. H. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1422737 · Cited by 131
How to Make Causal Inferences with Time-Series Cross-Sectional Data Under Selection on Observables
Blackwell, M., & Glynn, A. (2018) · American Political Science Review · doi:10.1017/s0003055418000357 · Cited by 131
Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data
Hong, G. (2010) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998609359785 · Cited by 130
Epidemiology and quantitative risk assessment: a bridge from science to policy.
Hertz‐Picciotto, I. (1995) · American Journal of Public Health · doi:10.2105/ajph.85.4.484 · Cited by 130
Evaluating (weighted) dynamic treatment effects by double machine learning
Bodory, H., Huber, M., & Lafférs, L. (2022) · Econometrics Journal · doi:10.1093/ectj/utac018 · Cited by 127
Rosenblum, M. D., Deeks, S. G., van der Laan, M. J., & Bangsberg, D. R. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007196 · Cited by 125
Chatton, A., Leborgne, F., Leyrat, C., Gillaizeau, F., Rousseau, C., Barbin, L., Laplaud, D. A., Léger, M., Giraudeau, B., & Foucher, Y. (2020) · Scientific Reports · doi:10.1038/s41598-020-65917-x · Cited by 137
Why prefer double robust estimators in causal inference?
Neugebauer, R. S., & van der Laan, M. J. (2004) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2004.06.060 · Cited by 125
ltmle : An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data
Lendle, S., Schwab, J., Petersen, M. L., & van der Laan, M. J. (2017) · Journal of Statistical Software · doi:10.18637/jss.v081.i01 · Cited by 124
Recent Developments in Causal Inference and Machine Learning
Brand, J. E., Zhou, X., & Xie, Y. F. (2023) · Annual Review of Sociology · doi:10.1146/annurev-soc-030420-015345 · Cited by 132
Graphical Models for Marked Point Processes Based on Local Independence
Didelez, V. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00634.x · Cited by 124
On the Validity of Covariate Adjustment for Estimating Causal EffectsPreprint
Shpitser, I., VanderWeele, T. J., & Robins, J. M. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1203.3515 · Cited by 124
Self-citationWomen, work, and well-being 1950–2000:
Klumb, P. L., & Lampert, T. (2003) · Social Science & Medicine · doi:10.1016/s0277-9536(03)00262-4 · Cited by 126
Marginal structural models in clinical research: when and how to use them?
Williamson, T. S., & Ravani, P. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw341 · Cited by 121
Cole, S. R., Hernán, M. Á., Margolick, J. B., Cohen, M. H., & Robins, J. M. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi216 · Cited by 120
Self-citationWestreich, D. J., Cole, S. R., Young, J. G., Palella, F. J., Tien, P. C., Kingsley, L., Gange, S. J., & Hernán, M. Á. (2012) · Statistics in Medicine · doi:10.1002/sim.5316 · Cited by 120
Causal Inference: A Missing Data Perspective
Ding, P., & Li, F. (2018) · Statistical Science · doi:10.1214/18-sts645 · Cited by 133
Tsai, A. C., Weiser, S. D., Petersen, M. L., Ragland, K. E., Kushel, M., & Bangsberg, D. R. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.160 · Cited by 119
Design and Analysis of Experiments
Mattei, A., Mealli, F., & Nodehi, A. (2021) · Springer eBooks · doi:10.1007/978-3-319-57365-6_40-1 · Cited by 118
Detection of Unfaithfulness and Robust Causal Inference
Zhang, J., & Spirtes, P. (2008) · Minds and Machines · doi:10.1007/s11023-008-9096-4 · Cited by 118
On collapsibility and confounding bias in Cox and Aalen regression models
Martinussen, T., & Vansteelandt, S. (2013) · Lifetime Data Analysis · doi:10.1007/s10985-013-9242-z · Cited by 124
Estimability and estimation of excess and etiologic fractions
Robins, J. M., & Greenland, S. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080709 · Cited by 117
Self-citationTime Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
Bojinov, I., & Shephard, N. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1527225 · Cited by 116
The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models
Pearl, J. (2012) · Wiley series in probability and statistics · doi:10.1002/9781119945710.ch12 · Cited by 118
Young, J. G., Hernán, M. Á., & Robins, J. M. (2014) · Epidemiologic Methods · doi:10.1515/em-2012-0001 · Cited by 115
Self-citationDuration and Timing of Exposure to Neighborhood Poverty and the Risk of Adolescent Parenthood
Wodtke, G. T. (2013) · Demography · doi:10.1007/s13524-013-0219-z · Cited by 114
Semiparametric Theory and Empirical Processes in Causal Inference
Kennedy, E. H. (2016) · ICSA book series in statistics · doi:10.1007/978-3-319-41259-7_8 · Cited by 114
Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples With Apples
Lodi, S., Phillips, A. N., Lundgren, J. D., Logan, R. W., Sharma, S., Cole, S. R., Babiker, A. G., Law, M. G., Chu, H., Byrne, D. D., et al. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz100 · Cited by 113
Scientist’s guide to developing explanatory statistical models using causal analysis principles
Grace, J. B., & Irvine, K. M. (2019) · Ecology · doi:10.1002/ecy.2962 · Cited by 113
Robins, J. M. (2004) · Biometrika · doi:10.1093/biomet/91.4.763 · Cited by 113
Self-citationSuper-Learning of an Optimal Dynamic Treatment Rule
Luedtke, A. R., & van der Laan, M. J. (2016) · The International Journal of Biostatistics · doi:10.1515/ijb-2015-0052 · Cited by 113
A Framework for Dynamic Causal Inference in Political Science
Blackwell, M. (2012) · American Journal of Political Science · doi:10.1111/j.1540-5907.2012.00626.x · Cited by 112
CAUSAL INFERENCE, PATH ANALYSIS AND RECURSIVE STRUCTURAL EQUATIONS MODELS
Holland, P. W. (1988) · ETS Research Report Series · doi:10.1002/j.2330-8516.1988.tb00270.x · Cited by 112
Regression standardization with the R package stdReg
Sjölander, A. (2016) · European Journal of Epidemiology · doi:10.1007/s10654-016-0157-3 · Cited by 111
Causal Reasoning for Algorithmic FairnessPreprint
Loftus, J. R., Russell, C., Kusner, M. J., & Silva, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.05859 · Cited by 163
Principal Stratification -- Uses and Limitations
VanderWeele, T. J. (2011) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1329 · Cited by 109
Wang, L., Rotnitzky, A., Lin, X., Millikan, R. E., & Thall, P. F. (2012) · Journal of the American Statistical Association · doi:10.1080/01621459.2011.641416 · Cited by 108
“To compare the different switch rules used in the trial, we apply the counterfactual framework for causal inference as originally developed by Rubin (1978) for time-independent treatments and later extended by Robins (1986, 1987) for time-dependent treatments in longitudinal studies.”
BackgroundMethodInfluentialQian, T., Walton, A. E., Collins, L. M., Klasnja, P., Lanza, S. T., Nahum-Shani, I. B., Rabbi, M., Russell, M. A., Walton, M. A., Yoo, H., et al. (2022) · Psychological Methods · doi:10.1037/met0000283 · Cited by 119
Estimation of the failure time distribution in the presence of informative censoring
Scharfstein, D. O. (2002) · Biometrika · doi:10.1093/biomet/89.3.617 · Cited by 108
Direct and Indirect Effects of Sequential TreatmentsPreprint
Didelez, V., Dawid, A. P., & Geneletti, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6840 · Cited by 108
Identification and estimation of survivor average causal effects
Tchetgen, E. J. T. (2014) · Statistics in Medicine · doi:10.1002/sim.6181 · Cited by 110
Correlation without a cause: an epidemiological odyssey
Smith, G. D., & Phillips, A. N. (2020) · International Journal of Epidemiology · doi:10.1093/ije/dyaa016 · Cited by 109
Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials
Hansford, H. J., Cashin, A. G., Jones, M. D., Swanson, S. A., Islam, N., Douglas, S. R. G., Rizzo, R. R. N., Devonshire, J. J., Williams, S. A., Dahabreh, I. J., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.36023 · Cited by 116
Lechner, M., & Miquel, R. (2009) · Empirical Economics · doi:10.1007/s00181-009-0297-3 · Cited by 107
Signed Directed Acyclic Graphs for Causal Inference
VanderWeele, T. J., & Robins, J. M. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2009.00728.x · Cited by 107
Self-citationCounterfactual graphical models for longitudinal mediation analysis with unobserved confounding
Shpitser, I. (2013) · Cognitive Science · doi:10.1111/cogs.12058 · Cited by 106
Causal Inference in the Health Sciences: A Conceptual Introduction
Pearl, J. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020315127304 · Cited by 109
Causal Reasoning with Ancestral Graphs
Zhang, J. (2008) · CaltechAUTHORS (California Institute of Technology) · doi:10.5555/1390681.1442780 · Cited by 106
Greiner, D. J., Pattanayak, C. W., & Hennessy, J. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.1948286 · Cited by 106
Difference-in-Differences Designs: A Practitioner’s Guide
Baker, A., Callaway, B., Cunningham, S., Goodman-Bacon, A., & Sant’Anna, P. H. C. (2026) · Journal of Economic Literature · doi:10.1257/jel.20251650 · Cited by 149
Invited Commentary: G-Computation-Lost in Translation?
Vansteelandt, S., & Keiding, N. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq474 · Cited by 105
Estimation of Controlled Direct Effects
Goetgeluk, S., Vansteelandt, S., & Goetghebeur, E. (2008) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00673.x · Cited by 104
Separable Effects for Causal Inference in the Presence of Competing Events
Stensrud, M. J., Young, J. G., Didelez, V., Robins, J. M., & Hernán, M. Á. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1765783 · Cited by 104
Self-citationA Theory of Statistical Inference for Matching Methods in Causal Research
Iacus, S. M., King, G., & Porro, G. (2018) · Political Analysis · doi:10.1017/pan.2018.29 · Cited by 107
Empirical and counterfactual conditions for sufficient cause interactions
Vanderweele, T. J., & Robins, J. M. (2008) · Biometrika · doi:10.1093/biomet/asm090 · Cited by 103
Self-citationPerković, E., Textor, J. C., Kalisch, M., & Maathuis, M. H. (2016) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000278021 · Cited by 103
Comparing two failure time distributions in the presence of dependent censoring
Lin, D. (1996) · Biometrika · doi:10.1093/biomet/83.2.381 · Cited by 103
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data
Lin, A., Jie Lu, B., Xuan, J., Zhu, F., & Zhang, G. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3379500 · Cited by 102
Head, J. R., Sondermeyer-Cooksey, G., Heaney, A. K., Yu, A. T., Jones, I. J., Bhattachan, A., Camponuri, S. K., Wagner, R., Mgbara, W., Phillips, S., et al. (2022) · The Lancet Planetary Health · doi:10.1016/s2542-5196(22)00202-9 · Cited by 101
Assessing mediation using marginal structural models in the presence of confounding and moderation.
Coffman, D. L., & Zhong, W. (2012) · Psychological Methods · doi:10.1037/a0029311 · Cited by 103
Causality, Mediation and Time: A Dynamic Viewpoint
Aalen, O. O., Røysland, K., Gran, J. M., & Ledergerber, B. (2012) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2011.01030.x · Cited by 101
Imbens, G. W. (2019) · National Bureau of Economic Research · doi:10.3386/w26104 · Cited by 103
G-computation demonstration in causal mediation analysis
Wang, A., & Arah, O. A. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0100-z · Cited by 112
Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification
Pearl, J. (1999) · Synthese · doi:10.1023/a:1005233831499 · Cited by 100
Nonparametric causal effects based on marginal structural models
Neugebauer, R. S., & van der Laan, M. J. (2006) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2005.12.008 · Cited by 100
A Structural Description of Biases That Generate Immortal Time
Hernán, M. A., Sterne, J. A. C., Higgins, J. P. T., Shrier, I., & Hernández-Díaz, S. (2025) · Epidemiology · doi:10.1097/ede.0000000000001808 · Cited by 99
The Relative Performance of Targeted Maximum Likelihood Estimators
Porter, K. E., Gruber, S., van der Laan, M. J., & Sekhon, J. S. (2011) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1308 · Cited by 103
Shi, X., Miao, W., Nelson, J. C., & Tchetgen, E. J. T. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12361 · Cited by 105
Witteman, J. C. M., D’Agostino, R. B., Stijnen, T., Kannel, W. B., Cobb, J. L., de Ridder, M. A. J., Hofman, A., & Robins, J. M. (1998) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a009658 · Cited by 99
Self-citationPrincipal Stratification -- a Goal or a Tool?
Pearl, J. (2011) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1322 · Cited by 98
To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies
Etminan, M., Brophy, J. M., Collins, P. G. S., Nazemipour, M., & Mansournia, M. A. (2021) · American Heart Journal · doi:10.1016/j.ahj.2021.03.008 · Cited by 98
Left Truncation, Susceptibility, and Bias in Occupational Cohort Studies
Applebaum, K. M., Malloy, E. J., & Eisen, E. A. (2011) · Epidemiology · doi:10.1097/ede.0b013e31821d0879 · Cited by 97
Causal models adjusting for time-varying confounding—a systematic review of the literature
Clare, P. J., Dobbins, T., & Mattick, R. P. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy218 · Cited by 95
Swanson, S. A., Hernán, M. A., Miller, M., Robins, J. M., & Richardson, T. S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1434530 · Cited by 93
Self-citationOn Granger causality and the effect of interventions in time series
Eichler, M., & Didelez, V. (2009) · Lifetime Data Analysis · doi:10.1007/s10985-009-9143-3 · Cited by 93
Multi‐state models and outcome prediction in bone marrow transplantation
Keiding, N., Klein, J. P., & Horowitz, M. M. (2001) · Statistics in Medicine · doi:10.1002/sim.810 · Cited by 93
Naimi, A. I., Kaufman, J. S., & MacLehose, R. F. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu107 · Cited by 95
Body Mass Index, Diabetes, and Mortality in French Women
Lajous, M., Bijon, A., Fagherazzi, G., Boutron‐Ruault, M., Balkau, B., Clavel‐Chapelon, F., & Hernán, M. Á. (2013) · Epidemiology · doi:10.1097/ede.0000000000000031 · Cited by 93
McGrath, S., Lin, V., Zhang, Z., Petito, L. C., Logan, R. W., Hernán, M. Á., & Young, J. G. (2020) · Patterns · doi:10.1016/j.patter.2020.100008 · Cited by 92
Controlled Direct and Mediated Effects: Definition, Identification and Bounds
VanderWeele, T. J. (2010) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2010.00722.x · Cited by 103
Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population
Dahabreh, I. J., Haneuse, S., Robins, J. M., Robertson, S. E., Buchanan, A. L., Stuart, E. A., & Hernán, M. Á. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa270 · Cited by 106
Self-citationSørensen, A. I. V., Spiliopoulos, L., Bager, P., Nielsen, N. M., Hansen, J. V., Koch, A., Meder, I. K., Ethelberg, S., & Hviid, A. P. (2022) · Nature Communications · doi:10.1038/s41467-022-31897-x · Cited by 96
A Method for Detection of Residual Confounding in Time-series and Other Observational Studies
Flanders, W. D., Klein, M., Darrow, L. A., Strickland, M. J., Sarnat, S., Sarnat, J. A., Waller, L. A., Winquist, A. G., & Tolbert, P. E. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181fdcabe · Cited by 95
Observation plans in longitudinal studies with time-varying treatments
Hernán, M. Á., McAdams‐DeMarco, M., McGrath, N. M., Lanoy, É., & Costagliola, D. G. (2008) · Statistical Methods in Medical Research · doi:10.1177/0962280208092345 · Cited by 89
Robust Estimation of Inverse Probability Weights for Marginal Structural Models
Imai, K., & Ratkovic, M. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.956872 · Cited by 94
A generalized back-door criterion
Maathuis, M. H., & Colombo, D. (2015) · The Annals of Statistics · doi:10.1214/14-aos1295 · Cited by 88
“This is known as the truncated factorization formula, see, e.g., Pearl (2000); Robins (1986); Spirtes et al. (1993).”
BackgroundBurgess, S., & Thompson, S. G. (2012) · Statistics in Medicine · doi:10.1002/sim.4498 · Cited by 87
Estimation of the effect of interventions that modify the received treatment
Haneuse, S., & Rotnitzky, A. G. (2013) · Statistics in Medicine · doi:10.1002/sim.5907 · Cited by 87
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
Dawid, A. P., & Didelez, V. (2010) · Statistics Surveys · doi:10.1214/10-ss081 · Cited by 87
Smith, M. J., Mansournia, M. A., Maringe, C., Zivich, P. N., Cole, S. R., Leyrat, C., Belot, A., Rachet, B., & Luque-Fernández, M. Á. (2021) · Statistics in Medicine · doi:10.1002/sim.9234 · Cited by 93
Rosenblum, M., & van der Laan, M. J. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1138 · Cited by 85
The Connected Brain: Causality, models, and intrinsic dynamics
Razi, A., & Friston, K. J. (2016) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2482121 · Cited by 90
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesPreprint
Kallus, N., & Uehara, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.08526 · Cited by 85
Galles, D., & Pearl, J. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00047-7 · Cited by 84
Ozenne, B., Scheike, T., Stærk, L., & Gerds, T. A. (2020) · Biometrical Journal · doi:10.1002/bimj.201800298 · Cited by 90
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
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