Identifiability, exchangeability and confounding revisited

[1]  Sander Greenland,et al.  Causal Diagrams , 2011, International Encyclopedia of Statistical Science.

[2]  Sander Greenland,et al.  Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods. , 2009, International journal of epidemiology.

[3]  Sander Greenland,et al.  Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources , 2009, 1001.2685.

[4]  Tyler J. VanderWeele,et al.  Marginal Structural Models for the Estimation of Direct and Indirect Effects , 2009, Epidemiology.

[5]  Sander Greenland,et al.  Case–Control Studies , 2008 .

[6]  J. Robins,et al.  Causal Directed Acyclic Graphs and the Direction of Unmeasured Confounding Bias , 2008, Epidemiology.

[7]  Daniel F. McCaffrey,et al.  Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data , 2008, 0804.2962.

[8]  Sander Greenland,et al.  Invited commentary: variable selection versus shrinkage in the control of multiple confounders. , 2007, American journal of epidemiology.

[9]  Sarah Boslaugh,et al.  Encyclopedia of epidemiology , 2008 .

[10]  Marie Davidian,et al.  Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. , 2008, Statistical science : a review journal of the Institute of Mathematical Statistics.

[11]  P. Gustafson,et al.  Bayesian sensitivity analysis for unmeasured confounding in observational studies , 2007, Statistics in medicine.

[12]  Chittaranjan Andrade,et al.  Confounding , 2007, Indian journal of psychiatry.

[13]  Sander Greenland,et al.  The Performance of Random Coefficient Regression in Accounting for Residual Confounding , 2006, Biometrics.

[14]  Mark J van der Laan,et al.  Estimation of Direct Causal Effects , 2006, Epidemiology.

[15]  J. Robins,et al.  Doubly Robust Estimation in Missing Data and Causal Inference Models , 2005, Biometrics.

[16]  M. Hernán.  Invited commentary: hypothetical interventions to define causal effects--afterthought or prerequisite? , 2005, American journal of epidemiology.

[17]  Sander Greenland,et al.  An Overview of Methods for Causal Inference from Observational Studies , 2005 .

[18]  Andrew Gelman,et al.  Applied Bayesian Modeling And Causal Inference From Incomplete-Data Perspectives , 2005 .

[19]  S. Greenland,et al.  Epidemiologic measures and policy formulation: lessons from potential outcomes , 2005, Emerging themes in epidemiology.

[20]  Sander Greenland,et al.  Multiple‐bias modelling for analysis of observational data , 2005 .

[21]  J. Robins,et al.  A Structural Approach to Selection Bias , 2004, Epidemiology.

[22]  J. Robins,et al.  Sensitivity Analyses for Unmeasured Confounding Assuming a Marginal Structural Model for Repeated Measures , 2022 .

[23]  R. Berk.  Regression Analysis: A Constructive Critique , 2003 .

[24]  S. Greenland.  Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias , 2003, Epidemiology.

[25]  James M. Robins,et al.  Unified Methods for Censored Longitudinal Data and Causality , 2003 .

[26]  S. Greenland,et al.  Estimating causal effects. , 2002, International journal of epidemiology.

[27]  S. Cole,et al.  Fallibility in estimating direct effects. , 2002, International journal of epidemiology.

[28]  M. Hernán,et al.  Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. , 2002, American journal of epidemiology.

[29]  J. Vandenbroucke.  The history of confounding. , 2002, Sozial- und Praventivmedizin.

[30]  P. Simpson,et al.  Statistical methods in cancer research , 2001, Journal of surgical oncology.

[31]  H. Morgenstern,et al.  Confounding in health research. , 2001, Annual review of public health.

[32]  James M. Robins,et al.  Conditioning, Likelihood, and Coherence: A Review of Some Foundational Concepts , 2000 .

[33]  S. Greenland.  When Should Epidemiologic Regressions Use Random Coefficients? , 2000, Biometrics.

[34]  J. Robins,et al.  Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.

[35]  S. Greenland.  Causal Analysis in the Health Sciences , 2000 .

[36]  S Greenland,et al.  Problems due to small samples and sparse data in conditional logistic regression analysis. , 2000, American journal of epidemiology.

[37]  S Greenland,et al.  Principles of multilevel modelling. , 2000, International journal of epidemiology.

[38]  J. Robins,et al.  Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models , 2000 .

[39]  J. Pearl,et al.  Confounding and Collapsibility in Causal Inference , 1999 .

[40]  S Greenland,et al.  Multilevel modeling and model averaging. , 1999, Scandinavian journal of work, environment & health.

[41]  G. Shaw,et al.  Maternal pesticide exposure from multiple sources and selected congenital anomalies. , 1999 .

[42]  J. Pearl,et al.  Causal diagrams for epidemiologic research. , 1999, Epidemiology.

[43]  J. Pearl.  Graphs, Causality, and Structural Equation Models , 1998 .

[44]  J. Robins,et al.  Toward a curse of dimensionality appropriate (CODA) asymptotic theory for semi-parametric models. , 1997, Statistics in medicine.

[45]  Markku Mikael Nurminen.  On the epidemiologic notion of confounding and confounder identification. , 1997, Scandinavian journal of work, environment & health.

[46]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[47]  S. Senn.  Testing for baseline balance in clinical trials. , 1994, Statistics in medicine.

[48]  J. Robins,et al.  Adjusting for differential rates of prophylaxis therapy for PCP in high- versus low-dose AZT treatment arms in an AIDS randomized trial , 1994 .

[49]  D. Clayton,et al.  Statistical Models in Epidemiology , 1993 .

[50]  J. Pearl.  [Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention , 1993 .

[51]  J. Robins,et al.  G-Estimation of the Effect of Prophylaxis Therapy for Pneumocystis carinii Pneumonia on the Survival of AIDS Patients , 1992, Epidemiology.

[52]  J. Robins,et al.  Estimating exposure effects by modelling the expectation of exposure conditional on confounders. , 1992, Biometrics.

[53]  J. Robins,et al.  Identifiability and Exchangeability for Direct and Indirect Effects , 1992, Epidemiology.

[54]  Sander Greenland,et al.  On the Logical Justification of Conditional Tests for Two-By-Two Contingency Tables , 1991 .

[55]  J. Robins,et al.  Correcting for non-compliance in randomized trials using rank preserving structural failure time models , 1991 .

[56]  S Greenland,et al.  Randomization, Statistics, and Causal Inference , 1990, Epidemiology.

[57]  D. Rubin.  [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

[58]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[59]  J M Robins,et al.  Confidence intervals for causal parameters. , 1988, Statistics in medicine.

[60]  S Greenland,et al.  Interpretation and choice of effect measures in epidemiologic analyses. , 1987, American journal of epidemiology.

[61]  J. Robins,et al.  The foundations of confounding in epidemiology , 1987 .

[62]  J. Robins.  A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. , 1987, Journal of chronic diseases.

[63]  J M Robins,et al.  Identifiability, exchangeability, and epidemiological confounding. , 1986, International journal of epidemiology.

[64]  J M Robins,et al.  The role of model selection in causal inference from nonexperimental data. , 1986, American journal of epidemiology.

[65]  P. Holland.  Statistics and Causal Inference , 1985 .

[66]  J M Robins,et al.  Confounding and misclassification. , 1985, American journal of epidemiology.

[67]  J. Schlesselman,et al.  Case-Control Studies: Design, Conduct, Analysis , 1982 .

[68]  O. Miettinen,et al.  Confounding: essence and detection. , 1981, American journal of epidemiology.

[69]  N. Breslow,et al.  Statistical methods in cancer research: volume 1- The analysis of case-control studies , 1980 .

[70]  R Neutra,et al.  Control of confounding in the assessment of medical technology. , 1980, International journal of epidemiology.

[71]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[72]  K J Rothman,et al.  Epidemiologic methods in clinical trials , 1977, Cancer.

[73]  J. Cornfield.  Recent methodological contributions to clinical trials. , 1976, American journal of epidemiology.

[74]  M. Nussbaum.  De Finetti, B.: Theory of Probability. John Wiley & Sons, London‐New York‐Sydney‐Toronto 1974. XIX, 300 S., £7,50 , 1975 .

[75]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[76]  Edward E. Leamer,et al.  False Models and Post-Data Model Construction , 1974 .

[77]  J. Copas.  Randomization models for the matched and unmatched 2 × 2 tables , 1973 .

[78]  J. Cornfield.  The University Group Diabetes Program. A further statistical analysis of the mortality findings. , 1971, JAMA.

[79]  L. Smelo.  University group diabetes program. , 1971, JAMA.

[80]  B. Macmahon,et al.  Epidemiology: Principles and Methods , 1970 .

[81]  Leslie Kish,et al.  Some Statistical Problems in Research Design , 1959 .

[82]  E. C. Hammond,et al.  Smoking and lung cancer: recent evidence and a discussion of some questions. , 1959, Journal of the National Cancer Institute.

[83]  D. R. Cox,et al.  Planning of Experiments , 1959 .

[84]  J. Wishart,et al.  Statistics in Research. , 1956 .

[85]  M. B. Wilk,et al.  THE RANDOMZATION ANALYSIS OF A GENERALIZED RANDOMIZED BLOCK DESIGN , 1955 .

[86]  B. L. Welch.  ON THE z-TEST IN RANDOMIZED BLOCKS AND LATIN SQUARES , 1937 .

[87]  R Fisher,et al.  Design of Experiments , 1936 .

[88]  G. Yule.  NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS , 1903 .

[89]  H. Jeffreys.  The Theory of Probability , 1896 .