Doubly robust estimation in causal inference with missing outcomes: With an application to the Aerobics Center Longitudinal Study
Author
Abstract
Suggested Citation
DOI: 10.1016/j.csda.2021.107399
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
- Alberto Abadie & Guido W. Imbens, 2016.
"Matching on the Estimated Propensity Score,"
Econometrica, Econometric Society, vol. 84, pages 781-807, March.
- Alberto Abadie & Guido W. Imbens, 2009. "Matching on the Estimated Propensity Score," NBER Working Papers 15301, National Bureau of Economic Research, Inc.
- Shu Yang & Jae Kwang Kim, 2016. "Likelihood-based Inference with Missing Data Under Missing-at-Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 436-454, June.
- Gruber Susan & van der Laan Mark J., 2010. "A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-18, August.
- Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
- Guido W. Imbens, 2004.
"Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review,"
The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
- Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
- Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
- 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.
- Karel Vermeulen & Stijn Vansteelandt, 2015. "Bias-Reduced Doubly Robust Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1024-1036, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Difang Huang & Jiti Gao & Tatsushi Oka, 2022.
"Semiparametric Single-Index Estimation for Average Treatment Effects,"
Papers
2206.08503, arXiv.org, revised Apr 2024.
- Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
- Shu Yang & Yunshu Zhang, 2023. "Multiply robust matching estimators of average and quantile treatment effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 235-265, March.
- Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017.
"The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation,"
Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
- Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2015. "The finite sample performance of semi- and nonparametric estimators for treatment effects and policy evaluation," FSES Working Papers 454, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2015. "The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation," IZA Discussion Papers 8756, Institute of Labor Economics (IZA).
- Farrell, Max H., 2015.
"Robust inference on average treatment effects with possibly more covariates than observations,"
Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
- Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
- Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
- Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
- Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010.
"How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score,"
IZA Discussion Papers
5268, Institute of Labor Economics (IZA).
- Martin Huber & Michael Lechner & Conny Wunsch, 2010. "How to control for many covariates? Reliable estimators based on the propensity score," University of St. Gallen Department of Economics working paper series 2010 2010-30, Department of Economics, University of St. Gallen.
- Y Cui & E J Tchetgen Tchetgen, 2024. "Selective machine learning of doubly robust functionals," Biometrika, Biometrika Trust, vol. 111(2), pages 517-535.
- Sant’Anna, Pedro H.C. & Zhao, Jun, 2020.
"Doubly robust difference-in-differences estimators,"
Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
- Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
- Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
- Yihui He & Fang Han, 2023. "On propensity score matching with a diverging number of matches," Papers 2310.14142, arXiv.org, revised Nov 2023.
- Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
- Xiaogang Duan & Guosheng Yin, 2017. "Ensemble Approaches to Estimating the Population Mean with Missing Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 899-917, December.
- Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.
- Susan Gruber & Mark J. van der Laan, 2013. "An Application of Targeted Maximum Likelihood Estimation to the Meta-Analysis of Safety Data," Biometrics, The International Biometric Society, vol. 69(1), pages 254-262, March.
- Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Testing Unconfoundedness Assumption Using Auxiliary Variables," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202004, University of Kansas, Department of Economics, revised Feb 2020.
- Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
- Susan Athey & Guido W. Imbens, 2017.
"The State of Applied Econometrics: Causality and Policy Evaluation,"
Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
- Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
- Huber, Martin, 2019.
"An introduction to flexible methods for policy evaluation,"
FSES Working Papers
504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
- Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
- Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
More about this item
Keywords
Average treatment effect; Average treatment effect on the treated; Causal inference; Missing data; Propensity score; Double robustness;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:168:y:2022:i:c:s0167947321002334. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.