Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator
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DOI: 10.31219/osf.io/yve6u
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- Daniel Vaughan-Whitehead, 2016. "Introduction," Economia & lavoro, Carocci editore, issue 2, pages 7-12.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011.
"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls,"
Papers
1201.0224, arXiv.org, revised May 2012.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 26/13, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 10/12, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP10/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP26/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
- Brookhart, M. Alan & van der Laan, Mark J., 2006. "A semiparametric model selection criterion with applications to the marginal structural model," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 475-498, January.
- Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
- Luke Keele & Dylan S. Small, 2021. "Comparing Covariate Prioritization via Matching to Machine Learning Methods for Causal Inference Using Five Empirical Applications," The American Statistician, Taylor & Francis Journals, vol. 75(4), pages 355-363, October.
- Po-Hsien Huang & Hung Chen & Li-Jen Weng, 2017. "A Penalized Likelihood Method for Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 329-354, June.
- Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
- Ben B. Hansen, 2004. "Full Matching in an Observational Study of Coaching for the SAT," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 609-618, January.
- Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2021-10-04 (Econometrics)
- NEP-ORE-2021-10-04 (Operations Research)
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