Randomization-based instrumental variables methods for binary outcomes with an application to the ‘IMPROVE’ trial
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- S. Vansteelandt & E. Goetghebeur, 2003. "Causal inference with generalized structural mean models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(4), pages 817-835, November.
- Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
- Hansen, Ben B. & Bowers, Jake, 2009. "Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 873-885.
- Paul S. Clarke & Frank Windmeijer, 2012.
"Instrumental Variable Estimators for Binary Outcomes,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1638-1652, December.
- Paul Clarke & Frank Windmeijer, 2009. "Instrumental Variable Estimators for Binary Outcomes," The Centre for Market and Public Organisation 09/209, The Centre for Market and Public Organisation, University of Bristol, UK.
- Paul Clarke & Frank Windmeijer, 2010. "Instrumental Variable Estimators for Binary Outcomes," The Centre for Market and Public Organisation 10/239, The Centre for Market and Public Organisation, University of Bristol, UK.
- Guido W. Imbens & Paul R. Rosenbaum, 2005. "Robust, accurate confidence intervals with a weak instrument: quarter of birth and education," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 109-126, January.
- Little, Roderick J A, 1985. "A Note about Models for Selectivity Bias," Econometrica, Econometric Society, vol. 53(6), pages 1469-1474, November.
- Freedman, David A. & Sekhon, Jasjeet S., 2010. "Endogeneity in Probit Response Models," Political Analysis, Cambridge University Press, vol. 18(2), pages 138-150, April.
- Small, Dylan S. & Ten Have, Thomas R. & Rosenbaum, Paul R., 2008. "Randomization Inference in a GroupRandomized Trial of Treatments for Depression: Covariate Adjustment, Noncompliance, and Quantile Effects," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 271-279, March.
- Tan, Zhiqiang, 2010. "Marginal and Nested Structural Models Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 157-169.
- Fan Yang & José R. Zubizarreta & Dylan S. Small & Scott Lorch & Paul R. Rosenbaum, 2014. "Dissonant Conclusions When Testing the Validity of an Instrumental Variable," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 253-263, November.
- Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
- Tan, Zhiqiang, 2006. "Regression and Weighting Methods for Causal Inference Using Instrumental Variables," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1607-1618, December.
- Paul Clarke & Frank Windmeijer, 2009.
"Identification of Causal Effects on Binary Outcomes Using Structural Mean Models,"
The Centre for Market and Public Organisation
09/217, The Centre for Market and Public Organisation, University of Bristol, UK.
- Paul S. Clarke & Frank Windmeijer, 2010. "Identification of causal effects on binary outcomes using structural mean models," CeMMAP working papers CWP02/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Rosenbaum, Paul R., 2007. "Interference Between Units in Randomized Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 191-200, March.
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- Hyunseung Kang & Laura Peck & Luke Keele, 2018. "Inference for instrumental variables: a randomization inference approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1231-1254, October.
- Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2023. "A Guide to Regression Discontinuity Designs in Medical Applications," Papers 2302.07413, arXiv.org, revised May 2023.
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