Robust Estimation of Inverse Probability Weights for Marginal Structural Models

Journal of the American Statistical Association

Volumn 110, Issue 511, 2015, Pages 1013-1023

  Imai, Kosuke ^a   Ratkovic, Marc ^a  

^a PRINCETON UNIVERSITY   (United States)

Author keywords

Causal inference; Covariate balancing propensity score; Inverse propensity score weighting; Observational studies; Sequential ignorability; Time dependent treatments

Indexed keywords

EID: 84946926453     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2014.956872     Document Type: Article

References (20)

1. M.Blackwell, (2013), A Framework for Dynamic Causal Inference in Political Science, American Journal of Political Science, 57, 504–520.
2. G.E.Box,, J.S.Hunter,, and W.G.Hunter, (2005), Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.) New York: Wiley-Interscience.
3. S.R.Cole,, M.A.Hernán, (2008), Constructing Inverse Probability Weights for Marginal Structural Models, American Journal of Epidemiology, 168, 656–664.
4. C.Fong,, M.Ratkovic,, K.Imai, (2014), CBPS: R Package for Covariate Balancing Propensity ScoreComprehensive R Archive Network (CRAN)Available at http://CRAN.R-project.org/package=CBPS.
5. I.J.Good, (1958), The Interaction Algorithm and Practical Fourier analysis, Journal of the Royal Statistical Society, Series B, 20, 361–372.
6. B.S.Graham,, C.Campos de Xavier Pinto,, D.Egel, (2012), Inverse Probability Tilting for Moment Condition Models With Missing Data, Review of Economic Studies, 79, 1053–1079.
7. J.Hainmueller, (2012), Entropy Balancing for Causal Effects: Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies, Political Analysis, 20, 25–46.
8. L.P.Hansen, (1982), Large Sample Properties of Generalized Method of Moments Estimators, Econometrica, 50, 1029–1054.
9. K.Hirano,, G.Imbens,, G.Ridder, (2003), Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score, Econometrica, 71, 1307–1338.
10. C.J.Howe,, S.R.Cole,, J.S.Chmiel,, A.Muñoz, (2011), Limitation of Inverse Probability-of-Censoring Weights in Estimating Survival in the Presence of Strong Selection Bias, American Journal of Epidemiology, 173, 569–577.
11. K.Imai,, M.Ratkovic, (2014), Covariate Balancing Propensity Score, Journal of the Royal Statistical Society, Series B, 76, 243–263.
12. J.D.Kang,, J.L.Schafer, (2007), Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean From Incomplete Data (with discussion), Statistical Science, 22, 523–539.
13. G.Lefebvre,, J.A.C.Delaney,, R.W.Platt, (2008), Impact of Mis-specification of the Treatment Model on Estimates From a Marginal Structural Model, Statistics in Medicine, 27, 3629–3642.
14. K.M.Mortimer,, R.Neugebauer,, M.van der Laan,, I.B.Tager, (2005), An Application of Model-fitting Procedures for Marginal Structural Models, American Journal of Epidemiology, 162, 382–388.
15. J.Neyman, (1923), On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9. (translated in 1990), Statistical Science, 5, 465–480.
16. J.Robins, (1999), “Marginal Structural Models Versus Structural Nested Models as Tools for Causal Inference,” in Statistical Models in Epidemiology, the Environment and Clinical Trials, eds. M.E.Halloran and D.A.Berry, New York: Springer, 95–134.
17. (1986), A New Approach to Causal Inference in Mortality Studies With Sustained Exposure Periods: Application to Control of the Healthy Worker Survivor Effect, Mathematical Modeling, 7, 1393–1512.
18. J.M.Robins,, M.A.Hernán,, B.Brumback, (2000), Marginal Structural Models and Causal Inference in Epidemiology, Epidemiology, 11, 550–560.
19. D.B.Rubin, (1973), Matching to Remove Bias in Observational Studies, Biometrics, 29, 159–183.
20. Z.Yu,, M.van der Laan, (2006), Double Robust Estimation in Longitudinal Marginal Structural Models, Journal of Statistical Planning and Inference, 136, 1061–1089.