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Statistical Learning in Causal Inference

For my publication list, click here. The following list is created for the beginners who might be interested in learning about statistical learning in causal inference.

Statistical Learning in Causal Inference Reference List
Disclaimer: This page does not host any files listed below and is an index of links to other websites that may host the relevant files / information. Subscription from corresponding publishers may be required to access the files. Feel free to report broken links or a link that you think should be removed. This is not meant to be a comprehensive list.

Statistical learning Books
  • James Gareth, Witten Daniela, Hastie Trevor, and Tibshirani Robert. An introduction to statistical learning. Springer, 2013. (link)
Propensity scores Books
  • Guo Shenyang and Fraser Mark W. Propensity score analysis: Statistical methods and applications, volume 12. Sage Publications, 2009. (link)
Suggestion of IPW improvement
  • Regier Michael D, Moodie Erica EM, and Platt Robert W. The effect of error-in-confounders on the estimation of the causal parameter when using marginal structural models and inverse probability-of-treatment weights: A simulation study. The international journal of biostatistics, pages 1–15, 2014.  (link)
  • Coffman D.L. and Zhong W. Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17(4):642–664, 2012. (link)
Propensity scores
  • Austin Peter C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research, 46(3):399–424, 2011. (link)
  • Li Lingling, Shen Changyu, Wu Ann C, and Li Xiaochun. Propensity score-based sensitivity analysis method for uncontrolled confounding. American journal of epidemiology, page kwr096, 2011. (link)
Propensity scores improvement
  • Lee Brian K, Lessler Justin, and Stuart Elizabeth A. Improving propensity score weighting using machine learning. Statistics in medicine, 29(3): 337–346, 2010. (link)
  • Westreich Daniel, Lessler Justin, and Funk Michele Jonsson. Propensity score estimation: machine learning and classification methods as alternatives to logistic regression. Journal of clinical epidemiology, 63(8):826, 2010. (link)
  • Keller Bryan SB, Kim Jee-Seon, and Steiner Peter M. Data mining alternatives to logistic regression for propensity score estimation: Neural networks and support vector machines. Multivariate Behavioral Research, 48(1):164–164, 2013. (link)
Tree-based methods
  • Watkins Stephanie, Jonsson-Funk Michele, Brookhart M Alan, Rosenberg Steven A, O’Shea T Michael, and Daniels Julie. An empirical comparison of tree-based methods for propensity score estimation. Health services research, 48(5):1798–1817, 2013. (link)
  • Luellen Jason K, Shadish William R, and Clark MH. Propensity scores an introduction and experimental test. Evaluation Review, 29(6):530–558, 2005. (link)
  • McCaffrey Daniel F, Ridgeway Greg, and Morral Andrew R. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological methods, 9(4):403, 2004. (link)
  • Zhu Yeying, Ghosh Debashis, Mukherjee Bhramar, and Mitra Nandita. A data-adaptive strategy for inverse weighted estimation of causal effects, 2013. URL Technical Report, Collection of Biostatistics Research Archive, last accessed: June-05-2014.  (link)
Robust IPW
IPW Truncation
  • Wang Y., Petersen M.L., Bangsberg D., and van der Laan M.J. Diagnosing bias in the inverse probability of treatment weighted estimator resulting from violation of experimental treatment assignment. UC Berkeley Division of Biostatistics Working Paper Series, (211):1–23, 2006. (link)
  • Bembom Oliver and van der Laan Mark J. Data-adaptive selection of the truncation level for inverse-probability-of-treatment-weighted estimators. 2008. (link)
General machine learning methods
  • Langford John and Zadrozny Bianca. Estimating class membership probabilities using classifier learners. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 198–205, 2005. (link)
  • Zhu Ji and Hastie Trevor. Kernel logistic regression and the import vector machine. Journal of Computational and Graphical Statistics, 14(1), 2005. (link)