Statistical science

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  Año 2020, Vol. 35, Número 3. Dedicado a: Causal Inference

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    Matching Methods for Observational Studies Derived from Large Administrative Databases

    Ruoqi Yu, Jeffrey H. Silber, Paul R. Rosenbaum

    • Resumen

    págs. 338-355

    
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    Comment: Matching Methods for Observational Studies Derived from Large Administrative Databases

    Fredrik Sävje

    págs. 356-360

    
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    Comment: Matching Methods for Observational Studies Derived from Large Administrative Databases

    Mark M. Fredrickson, Josh Errickson, Ben B. Hansen

    págs. 361-366

    
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    Commentary on Yu et al.: Opportunities and Challenges for Matching Methods in Large Databases

    Elizabeth A. Stuart, Benjamin Ackerman

    págs. 367-370

    
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    Rejoinder: Matching Methods for Observational Studies Derived from Large Administrative Databases

    Ruoqi Yu, Jeffrey H. Silber, Paul R. Rosenbaum

    págs. 371-374

    
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    Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study

    Tianchen Qian, Predrag Klasnja, Susan A. Murphy

    • Resumen

    págs. 375-390

    
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    Comment: Clarifying Endogeneous Data Structures and Consequent Modelling Choices Using Causal Graphs

    Erica E. M. Moodie, David A. Stephens

    págs. 391-393

    
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    Moving Toward Rigorous Evaluation of Mobile Health Interventions

    Kristin A. Linn

    • Resumen

    págs. 394-395

    
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    Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates

    Hunyong Cho, Joshua P. Zitovsky, Xinyi Li, Minxin Lu, Kushal Shah, John Sperger, Matthew C. B. Tsilimigras, Michael R. Kosorok

    • Resumen

    págs. 396-399

    
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    Rejoinder: Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study

    Tianchen Qian, Predrag Klasnja, Susan A. Murphy

    págs. 400-403

    
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    Invariance, Causality and Robustness

    Peter Bühlmann

    • Resumen

    págs. 404-426

    
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    Comment: Invariance, Causality and Robustness

    Vanessa Didelez

    págs. 427-429

    
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    Comment: Invariance and Causal Inference

    Stefan Wager

    págs. 430-433

    
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    Rejoinder: Invariance, Causality and Robustness

    Peter Bühlmann

    • Resumen

    págs. 434-436

    
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    Outcome-Wide Longitudinal Designs for Causal Inference: A New Template for Empirical Studies

    Tyler J. VanderWeele, Maya B. Mathur, Ying Chen

    • Resumen

    págs. 437-466

    
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    Comment: On the Potential for Misuse of Outcome-Wide Study Designs, and Ways to Prevent It

    Stijn Vansteelandt, Oliver Dukes

    págs. 467-471

    
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    Comment: Outcome-Wide Individualized Treatment Strategies

    Ashkan Ertefaie, Brent A. Johnson

    págs. 472-475

    
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    A New Template for Empirical Studies: From positivity to Positivity

    Rhian Daniel

    págs. 476-478

    
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    Rejoinder: The Future of Outcome-Wide Studies

    Tyler J. VanderWeele, Maya B. Mathur, Ying Chen

    págs. 479-483

    
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    A Nonparametric Super-Efficient Estimator of the Average Treatment Effect

    David Benkeser, Weixian Cai, Mark J. van der Laan

    • Resumen

    págs. 484-495

    
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    Comment: Increasing Real World Usage of Targeted Minimum Loss-Based Estimators

    Mireille E. Schnitzer

    págs. 496-498

    
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    Comment: Automated Analyses: Because We Can, Does It Mean We Should?

    Susan M. Shortreed, Erica E. M. Moodie

    págs. 499-502

    
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    Comment: Stabilizing the Doubly-Robust Estimators of the Average Treatment Effect under Positivity Violations

    Fan Li

    • Resumen

    págs. 503-510

    
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    Rejoinder: A Nonparametric Superefficient Estimator of the Average Treatment Effect

    David Benkeser, Weixian Cai, Mark J. van der Laan

    págs. 511-517

    
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    On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning

    Lin Liu, Rajarshi Mukherjee, James M. Robins

    • Resumen

    págs. 518-539

    
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    Discussion of “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning”

    Edward H. Kennedy, Sivaraman Balakrishnan, Larry Wasserman

    págs. 540-544

    
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    Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

    Lin Liu, Rajarshi Mukherjee, James M. Robins

    págs. 545-554