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We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of covariates from observational data.
On the consistency of supervised learning with missing values
Josse, J., N. Prost, E. Scornet, and G. Varoquaux (2019) · 1902
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Little, M. A. and R. Badawy (2019) · 1910
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Global,regional,and national disability- adjusted life years(dalys) for 359 diseases and injuries and healthy life expectancy(hale) for 195 countries and territories,1990-2017: a systematic analysis for the global burden of disease study 2017
Leigh, J., G. Collaborators, Y. Guo, K. Deribe, A. Brazinova, and S. Hostiuc (2018, 11) · 1922
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Inference and missing data
Rubin, D. B. (1976) · 1976
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Maximum likelihood from incomplete data via the em algorithm
Dempster, A. P., N. M. Laird, and D. B. Rubin (1977) · 1977
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Reducing bias in observational studies using subclassification on the propensity score
Rosenbaum, P. R. and D. B. Rubin (1984) · 1984
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A new approach to causal inference in mortality studies with a sustained exposure period–application to control of the healthy worker survivor effect
Robins, J. (1986) · 1986
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Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · 1987
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An introduction to the bootstrap
Efron, B. and R. J. Tibshirani (1994) · 1994
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Estimating and using propensity scores with partially missing data
D’Agostino, Jr, R. B. and D. B. Rubin (2000) · 2000
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Random forests
Breiman, L. (2001) · 2001
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FactoMineR: A package for multivariate analysis
Lê, S., J. Josse, and F. Husson (2008) · 2008
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Good methods for coping with missing data in decision trees
Twala, B., M. Jones, and D. J. Hand (2008) · 2008
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How to impute interactions, squares, and other transformed variables
Hippel, P. v. (2009) · 2009
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Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing
Mattei, A. (2009) · 2009
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Effects of tranexamic acid on death, vascular occlusive events, and blood transfusion in trauma patients with significant haemorrhage (CRASH-2): A randomised, placebo-controlled trial
Shakur-Still, H., I. Roberts, R. Bautista, J. Caballero, T. Coats, Y. Dewan, H. El-Sayed, G. Tamar, S. Gupta, J. Herrera, B. Hunt, P. Iribhogbe, M. Izurieta, H. Khamis, E. Komolafe, M. Marrero, J. Mejía-Mantilla, J. J. Miranda, C. Uribe, and S. Yutthakasemsunt (2009, 11) · 2009
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Association of preexisting medical conditions with in-hospital mortality in multiple-trauma patients
Wutzler, S., M. Maegele, I. Marzi, T. Spanholtz, A. Wafaisade, R. Lefering, T. R. of the German Society for Trauma Surgery, et al. (2009) · 2009
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Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial
Cole, S. R. and E. A. Stuart (2010) · 2010
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Analysis of incomplete multivariate data
Schafer, J. L. (2010) · 2010
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Causal inference methods for combining randomized trials and observational studies: a review
Colnet, B., I. Mayer, G. Chen, A. Dieng, R. Li, G. Varoquaux, J.-P. Vert, J. Josse, and S. Yang (2020) · 2011
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The importance of early treatment with tranexamic acid in bleeding trauma patients: an exploratory analysis of the crash-2 randomised controlled trial
CRASH-2 Collaborators et al. (2011) · 2011
Cited alongside, same era.
The use of propensity scores to assess the generalizability of results from randomized trials
Stuart, E. A., S. R. Cole, C. P. Bradshaw, and P. J. Leaf (2011) · 2011
Cited alongside, same era.
mice: Multivariate imputation by chained equations in r
van Buuren, S. and K. Groothuis-Oudshoorn (2011) · 2011
Cited alongside, same era.
Statistical methods for handling incomplete Data
Kim, J. K. and J. Shao (2013) · 2013
Cited alongside, same era.
Changing patterns in the epidemiology of traumatic brain injury
Roozenbeek, B., A. I. Maas, and D. K. Menon (2013) · 2013
Cited alongside, same era.
Statistical Analysis with Missing Data
Crash-3: a win for patients with traumatic brain injury
Cap, A. P. (2019) · 2019
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Extending inferences from a randomized trial to a target population
Dahabreh, I. J. and M. A. Hernán (2019) · 2019
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On the relation between g-formula and inverse probability weighting estimators for generalizing trial results
Dahabreh, I. J., S. E. Robertson, and M. A. Hernán (2019) · 2019
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Propensity score analysis with partially observed covariates: How should multiple imputation be used?
Leyrat, C., S. R. Seaman, I. R. White, I. Douglas, L. Smeeth, J. Kim, M. Resche-Rigon, J. R. Carpenter, and E. J. Williamson (2019) · 2019
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Causal inference with confounders missing not at random
Yang, S., L. Wang, and P. Ding (2019) · 2019
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Little, R. J. and D. B. Rubin (2014) · 2014
Cited alongside, same era.
Inverse probability weighting with missing predictors of treatment assignment or missingness
Seaman, S. and I. White (2014) · 2014
Cited alongside, same era.
Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression
Bartlett, J. W., O. Harel, and J. R. Carpenter (2015) · 2015
Cited alongside, same era.
Endogenous plasminogen activators mediate progressive intracerebral hemorrhage after traumatic brain injury in mice
Hijazi, N., R. Abu Fanne, R. Abramovitch, S. Yarovoi, M. Higazi, S. Abdeen, M. Basheer, E. Maraga, D. B. Cines, and A. Al-Roof Higazi (2015) · 2015
Cited alongside, same era.
Clarifying missing at random and related definitions, and implications when coupled with exchangeability
Mealli, F. and D. B. Rubin (2015) · 2015
Cited alongside, same era.
Causal inference and the data-fusion problem
Bareinboim, E. and J. Pearl (2016) · 2016
Cited alongside, same era.
Using big data to emulate a target trial when a randomized trial is not available
Hernán, M. A. and J. M. Robins (2016) · 2016
Cited alongside, same era.
Blake, H. A., C. Leyrat, K. E. Mansfield, L. A. Tomlinson, J. Carpenter, and E. J. Williamson (2020) · 2020
Later among the works it cites.
Logistic regression with missing covariates—parameter estimation, model selection and prediction within a joint-modeling framework
Jiang, W., J. Josse, M. Lavielle, and T. Group (2020) · 2020
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Neumiss networks: differential programming for supervised learning with missing values
Le Morvan, M., J. Josse, T. Moreau, E. Scornet, and G. Varoquaux (2020) · 2020
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Doubly robust treatment effect estimation with missing attributes
Mayer, I., E. Sverdrup, T. Gauss, J.-D. Moyer, S. Wager, and J. Josse (2020) · 2020
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grf: Generalized Random Forests
Tibshirani, J., S. Athey, R. Friedberg, V. Hadad, D. Hirshberg, L. Miner, E. Sverdrup, S. Wager, and M. Wright (2020) · 2020
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A causal bootstrap
Imbens, G. and K. Menzel (2021) · 2021
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What’sa good imputation to predict with missing values?
Le Morvan, M., J. Josse, E. Scornet, and G. Varoquaux (2021) · 2021
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Covariate balancing sensitivity analysis for extrapolating randomized trials across locations
Nie, X., G. Imbens, and S. Wager (2021) · 2021
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Targeted optimal treatment regime learning using summary statistics
Chu, J., W. Lu, and S. Yang (2022) · 2022
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Causal effect on a target population: A sensitivity analysis to handle missing covariates
Colnet, B., J. Josse, G. Varoquaux, and E. Scornet (2022) · 2022
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Improving trial generalizability using observational studies
Lee, D., S. Yang, L. Dong, X. Wang, D. Zeng, and J. Cai (2022) · 2022
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R-miss-tastic: a unified platform for missing values methods and workflows
Mayer, I., J. Josse, N. Tierney, and N. Vialaneix (2022) · 2022
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A review of generalizability and transportability
Degtiar, I. and S. Rose (2023) · 2023
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High-dimensional principal component analysis with heterogeneous missingness
Zhu, Z., T. Wang, and R. J. Samworth (2022) · 2031
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Using simulation studies to evaluate statistical methods
Morris, T. P., I. R. White, and M. J. Crowther (2019) · 2074
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