Covariate association eliminating weights: A unified weighting framework for causal effect estimation

Biometrika

Volumn 105, Issue 3, 2018, Pages 709-722

  Yiu, Sean ^a   Su, Li ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Causal inference; Confounding; Continuous treatment; Covariate balance; Inverse probability weighting; Propensity function

Indexed keywords

EID: 85054957049     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asy015     Document Type: Article

References (37)

1. BRUCE, I. N., O'KEEFFE, A. G., FAREWELL, V., HANLY, J. G., MANZI, S., SU, L., GLADMAN, D. D., BAE, S.-C., SANCHEZGUERRERO, J., ROMERO-DIAZ, J. et al. (2015). Factors associated with damage accrual in patients with systemic lupus erythematosus: Results from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort. Ann. Rheum. Dis. 74, 1706-13.
2. CHAN, K. C. G.,YAM, S. C. P. & ZHANG, Z. (2016). Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting. J. R. Statist. Soc. B 78, 673-700.
3. CRUMP, R. K., HOTZ,V. J., IMBENS, G.W.& MITNIK, O.A. (2009). Dealing with limited overlap in estimation of average treatment effects. Biometrika 96, 187-99.
4. DE LUNA, X.,WAERNBAUM, I. & RICHARDSON, T. S. (2011). Covariate selection for the nonparametric estimation of an average treatment effect. Biometrika 98, 861-75.
5. DING, P.&MIRATRIX, L.W. (2015).To adjust or not to adjust? Sensitivity analysis of M-bias and butterfly-bias. J. Causal Infer. 3, 41-57.
6. EHRENSTEIN, M. R., CONROY, S. E., HEATH, J., LATCHMAN, D. S. & ISENBERG, D. A. (1995). The occurrence, nature and distribution of flares in a cohort of patients with systemic lupus erythematosus. Br. J. Rheumatol. 34, 257-60.
7. FONG, C., HAZLETT, C. & IMAI, K. (2018). Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements. Ann. Appl. Statist. 12, 156-77.
8. GRAHAM, B. S., CAMPOS DE XAVIER PINTO, C. & EGEL, D. (2012). Inverse probability tilting for moment condition models with missing data. Rev. Econ. Stud. 79, 1053-79.
9. GREENLAND, S. & POOLE, C. (1995). Interpretation and analysis of differential exposure variability and zero-exposure categories for continuous exposures. Epidemiology 6, 326-8.
10. HAINMUELLER, J. (2012). Entropy balancing for causal effects: Multivariate reweighting method to produce balanced samples in observational studies. Polit. Anal. 20, 24-46.
11. HAY, E., BACON, P., GORDAN, C., ISENBERG, D. A., MADDISON, P., SNAITH, M. L., SYMMONS, D. P.,VINER, N. & ZOMA, A. (1993). The BILAG index: A reliable and validated instrument for measuring clinical disease activity in systemic lupus erythematosus. Quart. J. Med. 86, 447-58.
12. IMAI, K. & RATKOVIC, M. (2014). Covariate balancing propensity score. J. R. Statist. Soc. B 76, 243-63.
13. IMAI, K. & RATKOVIC, M. (2015). Robust estimation of inverse probability weights for marginal structural models. J. Am. Statist. Assoc. 110, 1013-23.
14. IMAI, K. & vAN DYK, D.A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. J. Am. Statist. Assoc. 99, 854-66.
15. IMBENS, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-10.
16. KANG, J. D. & SCHAFER, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data (with Discussion). Statist. Sci. 22, 523-39.
17. LEE, B. K., LESSLER, J. & STUART, E.A. (2010). Improving propensity score weighting using machine learning. Statist. Med. 29, 337-46.
18. LU, B. (2005). Propensity score matching with time-dependent covariates. Biometrics 61, 721-8.
19. MCCAFFREY, D. F., GRIFFIN, B. A., ALMIRALL, D., SLAUGHTER, M. E., RAMCHARD, R. & BURGETTE, L. F. (2013). A tutorial on propensity score estimation for multiple treatment using generalized boosted models. Statist. Med. 32, 3388-414.
20. MOODIE, E. M. & STEPHENS, D. A. (2010). Estimation of dose-response functions for longitudinal data using the generalised propensity score. Statist. Meth. Med. Res. 21, 149-66.
21. NAIMI,A. I.,MOODIE, E. E.,AUGER,N.&KAUFMAN, J. S. (2014). Constructing inverse probability weights for continuous exposures: A comparison of methods. Epidemiology 25, 292-9.
22. OLSEN, M. K. & SCHAFER, J. L. (2001). A two-part random effects model for semicontinuous longitudinal data. J. Am. Statist. Assoc. 96, 730-45.
23. PEARL, J. (1995). Causal diagrams for empirical research. Biometrika 82, 669-88.
24. PETERSON, M. L., PORTER, K. E., GRUBER, S. G.,WANG, Y. &VAN DER LAAN, M. J. (2010). Diagnosing and responding to violations in the positivity assumption. Statist. Meth. Med. Res. 21, 31-54.
25. R DEVELOPMENT CORE TEAM (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org.
26. ROBINS, J., SUED, M., LEI-GOMEZ, Q. & ROTNITZKY, A. (2007). Comment: Performance of doubly-robust estimators when "inverse probability" weights are highly variable. Statist. Sci. 22, 544-59.
27. ROBINS, J. M. (2000). Marginal structural models versus structural nested models as tools for causal inference. In Statistical Models in Epidemiology, the Environment and Clinical Trials, M. E. Halloran & D. Berry, eds. New York: Springer, pp. 95-133.
28. ROBINS, J. M.,HERNÁN,M.A.&BRUMBACK,B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550-60.
29. ROSENBAUM, P. R. & RUBIN, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
30. SHPITSER, I.,VANDERWEELE,T. J. & ROBINS, J.M. (2010). On the validity of covariate adjustment for estimating causal effects. In Proceedings of the 26th Conference on Uncertainty and Artificial Intelligence, P. GrünwaldP. Spirtes, eds. AUAI Press, pp. 527-36.
31. SOETAERT, K., VAN DEN MEERSCHE, K. & VAN OEVELEN, D. (2009). limSolve: Solving Linear Inverse Models. R-package version 1.5.1.
32. STUART, E. A., COLE, S. R., BRADSHAW, C. P. & LEAF, P. J. (2011). The use of propensity scores to assess the generalizability of results from randomized trials. J. R. Statist. Soc. A 174, 369-86.
33. VANDERWEELE, T. J. & SHPITSER, I. (2011). A new criterion for confounder selection. Biometrics 67, 1406-13.
34. VANSTEELANDT, S., BEKAERT, M. & CLAESKENS, G. (2012). On model selection and model misspecification in causal inference. Statist. Meth. Med. Res. 21, 7-30.
35. ZHANG, Z., ZHOU, J., CAO,W. & ZHANG, J. (2016). Causal inference with a quantitative exposure. Statist. Meth. Med. Res. 25, 315-35.
36. ZHU,Y., COFFMAN, D. L. & GHOSH, D. (2015).A boosting algorithm for estimating generalized propensity scores with continuous treatments. J. Causal Infer. 3, 25-40.
37. ZUBIZARRETA, J. R. (2015). Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Statist. Assoc. 110, 910-22.