Causal Etiology of the Research of James M. Robins

This issue of Statistical Science draws its inspiration from the work of James M. Robins. Jon Wellner, the Editor at the time, asked the two of us to edit a special issue that would highlight the research topics studied by Robins and the breadth and depth of Robins' contributions. Between the two of us, we have collaborated closely with Jamie for nearly 40 years. We agreed to edit this issue because we recognized that we were among the few in a position to relate the trajectory of his research career to date.

[1]  M. J. Laan,et al.  Targeted Learning: Causal Inference for Observational and Experimental Data , 2011 .

[2]  J. Robins,et al.  Estimation and extrapolation of optimal treatment and testing strategies , 2008, Statistics in medicine.

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

[4]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[5]  J. Robins.  Structural Nested Failure Time Models , 2005 .

[6]  Marco Valtorta,et al.  Pearl's Calculus of Intervention Is Complete , 2006, UAI.

[7]  T. Tony Cai,et al.  Effect of mean on variance function estimation in nonparametric regression , 2006 .

[8]  J M Robins,et al.  Causal models for estimating the effects of weight gain on mortality , 2008, International Journal of Obesity.

[9]  J. Robins,et al.  Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models , 1999 .

[10]  Judea Pearl,et al.  Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models , 2006, AAAI.

[11]  Illtyd Trethowan.  Causality , 1938 .

[12]  Peter J. Bickel,et al.  INFERENCE FOR SEMIPARAMETRIC MODELS: SOME QUESTIONS AND AN ANSWER , 2001 .

[13]  James M. Robins,et al.  Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .

[14]  J. Robins.  Addendum to “a new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect” , 1987 .

[15]  Michael Rosenblum,et al.  Marginal Structural Models , 2011 .

[16]  Niels Keiding,et al.  Standardization and Control for Confounding in Observational Studies: A Historical Perspective , 2015, 1503.02853.

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

[18]  M J van der Laan,et al.  Covariate adjustment in randomized trials with binary outcomes: Targeted maximum likelihood estimation , 2009, Statistics in medicine.

[19]  J. Robins,et al.  Comparison of dynamic treatment regimes via inverse probability weighting. , 2006, Basic & clinical pharmacology & toxicology.

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[21]  M. Davidian,et al.  Covariate adjustment for two‐sample treatment comparisons in randomized clinical trials: A principled yet flexible approach , 2008, Statistics in medicine.

[22]  D. Rubin,et al.  Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes , 1999 .

[23]  James M. Robins,et al.  Optimal Structural Nested Models for Optimal Sequential Decisions , 2004 .

[24]  S Greenland,et al.  The probability of causation under a stochastic model for individual risk. , 1989, Biometrics.

[25]  T. Richardson,et al.  Markovian acyclic directed mixed graphs for discrete data , 2013, 1301.6624.

[26]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[27]  Lena Osterhagen,et al.  Multiple Imputation For Nonresponse In Surveys , 2016 .

[28]  Lie Wang,et al.  Variance function estimation in multivariate nonparametric regression with fixed design , 2009, J. Multivar. Anal..

[29]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[30]  Ellen MacKenzie,et al.  Principal Stratification Designs to Estimate Input Data Missing Due to Death , 2007, Biometrics.

[31]  John Langford,et al.  Doubly Robust Policy Evaluation and Learning , 2011, ICML.

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[35]  James M Robins,et al.  A Proof of Bell's Inequality in Quantum Mechanics Using Causal interactions , 2012, Scandinavian journal of statistics, theory and applications.

[36]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[37]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[38]  Jason P Fine,et al.  Nonparametric Bounds and Sensitivity Analysis of Treatment Effects. , 2014, Statistical science : a review journal of the Institute of Mathematical Statistics.

[39]  Stijn Vansteelandt,et al.  Structural nested models and G-estimation: the partially realized promise , 2014, 1503.01589.

[40]  Thomas S. Richardson,et al.  Learning high-dimensional directed acyclic graphs with latent and selection variables , 2011, 1104.5617.

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[42]  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.

[43]  M. J. van der Laan,et al.  Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules , 2007, The international journal of biostatistics.

[44]  James M. Robins,et al.  Parameter and Structure Learning in Nested Markov Models , 2012, 1207.5058.

[45]  J. Robins,et al.  Inference for imputation estimators , 2000 .

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[47]  D. Rubin,et al.  Principal Stratification in Causal Inference , 2002, Biometrics.

[48]  T. Richardson.  Markov Properties for Acyclic Directed Mixed Graphs , 2003 .

[49]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[50]  Elizabeth L. Ogburn,et al.  Causal diagrams for interference , 2014, 1403.1239.

[51]  James M. Robins,et al.  Invited Commentary: Effect Modification by Time-varying Covariates , 2007 .

[52]  M. Hudgens,et al.  Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials , 2003, Biometrics.

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[54]  Judea Pearl,et al.  Direct and Indirect Effects , 2001, UAI.

[55]  Jin Tian,et al.  Graphical Models for Inference with Missing Data , 2013, NIPS.

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[58]  O. Aalen.  Nonparametric Inference for a Family of Counting Processes , 1978 .

[59]  H. Prosper.  Bayesian Analysis , 2000, hep-ph/0006356.

[60]  J. Robins,et al.  Estimability and estimation of excess and etiologic fractions. , 1989, Statistics in medicine.

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

[62]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[63]  C. Cassel,et al.  Some results on generalized difference estimation and generalized regression estimation for finite populations , 1976 .

[64]  James M. Robins,et al.  Coarsening at Random: Characterizations, Conjectures, Counter-Examples , 1997 .

[65]  D. Rubin.  Direct and Indirect Causal Effects via Potential Outcomes * , 2004 .

[66]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

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[68]  J. Pearl,et al.  EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS , 2013 .

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

[71]  N. Wermuth.  PROBABILITY DISTRIBUTIONS WITH SUMMARY GRAPH STRUCTURE , 2010, 1003.3259.

[72]  J. Robins,et al.  Uniform consistency in causal inference , 2003 .

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

[74]  Eric J Tchetgen Tchetgen,et al.  Interference and Sensitivity Analysis. , 2014, Statistical science : a review journal of the Institute of Mathematical Statistics.

[75]  D.,et al.  Regression Models and Life-Tables , 2022 .

[76]  B. J. K. Kleijn,et al.  The Bayesian analysis of complex, high-dimensional models: Can it be CODA? , 2012, 1203.5471.

[77]  Judea Pearl,et al.  Probabilistic Evaluation of Counterfactual Queries , 1994, AAAI.

[78]  J. Robins,et al.  The International Journal of Biostatistics CAUSAL INFERENCE Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes , Part I : Main Content , 2011 .

[79]  Tyler J Vanderweele,et al.  Sufficient cause interactions for categorical and ordinal exposures with three levels. , 2010, Biometrika.

[80]  J. Robins,et al.  Minimal sufficient causation and directed acyclic graphs , 2009, 0906.1720.

[81]  Donald K. K. Lee,et al.  SHARP BOUNDS ON THE VARIANCE IN RANDOMIZED EXPERIMENTS , 2014, 1405.6555.

[82]  Judea Pearl,et al.  A Theory of Inferred Causation , 1991, KR.

[83]  James M. Robins,et al.  Asymptotic Distribution of P Values in Composite Null Models , 2000 .

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

[85]  Anastasios A. Tsiatis,et al.  Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes , 2012, Statistical science : a review journal of the Institute of Mathematical Statistics.

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

[87]  Jin Tian,et al.  Identifying Dynamic Sequential Plans , 2008, UAI.

[88]  S. Murphy,et al.  Optimal dynamic treatment regimes , 2003 .

[89]  James M. Robins,et al.  Large-sample theory for parametric multiple imputation procedures , 1998 .

[90]  J. Sekhon.  The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods , 2008 .

[91]  James M. Robins,et al.  Causal inference for complex longitudinal data: the continuous case , 2001 .

[92]  James M Robins,et al.  Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results , 2010, The international journal of biostatistics.

[93]  D B Rubin,et al.  More powerful randomization-based p-values in double-blind trials with non-compliance. , 1998, Statistics in medicine.

[94]  J. Robins.  Analytic Methods for Estimating HIV-Treatment and Cofactor Effects , 2002 .

[95]  M. J. Bayarri,et al.  P Values for Composite Null Models , 2000 .

[96]  P. Spirtes,et al.  Ancestral graph Markov models , 2002 .

[97]  J. Robins,et al.  Semiparametric regression estimation in the presence of dependent censoring , 1995 .

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

[99]  Tyler J VanderWeele,et al.  GENERAL THEORY FOR INTERACTIONS IN SUFFICIENT CAUSE MODELS WITH DICHOTOMOUS EXPOSURES. , 2012, Annals of statistics.

[100]  Niels Keiding,et al.  Statistical Models Based on Counting Processes , 1993 .

[101]  James M. Robins,et al.  Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs , 1997, UAI.

[102]  T. Richardson.  Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality , 2013 .

[103]  J. Robins,et al.  On the impossibility of inferring causation from association without background knowledge , 1999 .

[104]  J. Robins.  Correcting for non-compliance in randomized trials using structural nested mean models , 1994 .

[105]  J. Robins.  Estimation of the time-dependent accelerated failure time model in the presence of confounding factors , 1992 .

[106]  S. Lauritzen,et al.  Markov properties for mixed graphs , 2011, 1109.5909.

[107]  M. J. van der Laan,et al.  The International Journal of Biostatistics Targeted Maximum Likelihood Learning , 2011 .

[108]  David Firth,et al.  Robust models in probability sampling , 1998 .

[109]  James M Robins,et al.  A mapping between interactions and interference: implications for vaccine trials. , 2012, Epidemiology.

[110]  P. Spirtes,et al.  A uniformly consistent estimator of causal effects under the k-Triangle-Faithfulness assumption , 2014, 1502.00829.

[111]  D. Rubin,et al.  MULTIPLE IMPUTATIONS IN SAMPLE SURVEYS-A PHENOMENOLOGICAL BAYESIAN APPROACH TO NONRESPONSE , 2002 .

[112]  Elias Bareinboim,et al.  External Validity: From Do-Calculus to Transportability Across Populations , 2014, Probabilistic and Causal Inference.

[113]  James M. Robins,et al.  INTRODUCTION TO NESTED MARKOV MODELS , 2014 .

[114]  Judea Pearl,et al.  On the Testability of Causal Models With Latent and Instrumental Variables , 1995, UAI.

[115]  James M Robins,et al.  Locally Efficient Estimation of a Multivariate Survival Function in Longitudinal Studies , 2002 .

[116]  Jin Tian,et al.  A general identification condition for causal effects , 2002, AAAI/IAAI.

[117]  B. Efron,et al.  Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information , 1978 .

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

[119]  J. Robins,et al.  Estimation of Regression Coefficients When Some Regressors are not Always Observed , 1994 .

[120]  Richard D. Gill,et al.  Statistics, causality and Bell's theorem , 2012, 1207.5103.

[121]  D. Freedman.  Statistical Models for Causation , 2006, Evaluation review.

[122]  James M Robins,et al.  Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions , 2012, Journal of the American Statistical Association.

[123]  Aad van der Vaart,et al.  Higher Order Tangent Spaces and Influence Functions , 2014, 1502.00812.

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

[125]  D. Harrington,et al.  Counting Processes and Survival Analysis , 1991 .

[126]  J. Robins,et al.  Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers , 1992 .

[127]  Eric A. Cator.  On the testability of the CAR assumption , 2004 .

[128]  J. Robins,et al.  Alternative Graphical Causal Models and the Identification of Direct E!ects , 2010 .

[129]  A. Tsiatis.  Semiparametric Theory and Missing Data , 2006 .

[130]  Peter Bühlmann,et al.  Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm , 2007, J. Mach. Learn. Res..

[131]  James M. Robins,et al.  Observational Studies Analyzed Like Randomized Experiments: An Application to Postmenopausal Hormone Therapy and Coronary Heart Disease , 2008, Epidemiology.

[132]  Tyler J VanderWeele,et al.  Epistatic Interactions , 2010, Statistical applications in genetics and molecular biology.

[133]  Tyler J. VanderWeele,et al.  On the definition of a confounder , 2013, Annals of statistics.

[134]  Jin Tian,et al.  On the Testable Implications of Causal Models with Hidden Variables , 2002, UAI.

[135]  Mark J van der Laan,et al.  Locally Efficient Estimation With Bivariate Right-Censored Data , 2006 .

[136]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[137]  Aad van der Vaart,et al.  Higher order influence functions and minimax estimation of nonlinear functionals , 2008, 0805.3040.

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