CHEMIST: an R package for causal inference with high-dimensional error-prone covariates and misclassified treatments
Author:
Funder
National Science and Technology Council
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s42081-023-00217-y.pdf
Reference32 articles.
1. Baldé, I., Yang, Y. A., & Lefebvre, G. (2023). Reader reaction to “Outcome-adaptive lasso: Variable selection for causal inference’’ by Shortreed and Ertefaie (2017). Biometrics, 79, 514–520.
2. Braun, D., Gorfine, M., Parmigiani, G., Arvold, N. D., Dominici, F., & Zigler, C. (2017). Propensity scores with misclassified treatment assignment: A likelihood-based adjustment. Biostatistics, 18, 695–710.
3. Chatterjee, S. (2021). A new coefficient of correlation. Journal of the American Statistical Association, 116, 2009–2022.
4. Chen, L.-P. (2023a). A note of feature screening via rank-based coefficient of correlation. Biometrical Journal, 1–20.
5. Chen, L.-P. (2023b). Variable selection and estimation for misclassified binary responses and multivariate error-prone predictors. Journal of Computational and Graphical Statistics, 1–14.
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