REFERENCES
- Blackwell, M. (2013), A Framework for Dynamic Causal Inference in Political Science, American Journal of Political Science, 57, 504–520.
- Box, G. E., Hunter, J. S., and Hunter, W. G. (2005), Statistics for Experimenters: Design, Innovation, and Discovery ( 2nd ed.) New York: Wiley-Interscience.
- Cole, S.R., Hernán, M.A. (2008), Constructing Inverse Probability Weights for Marginal Structural Models, American Journal of Epidemiology, 168, 656–664.
- Fong, C., Ratkovic, M., Imai, K. (2014), CBPS: R Package for Covariate Balancing Propensity ScoreComprehensive R Archive Network (CRAN) Available at http://CRAN.R-project.org/package=CBPS.
- Good, I.J. (1958), The Interaction Algorithm and Practical Fourier analysis, Journal of the Royal Statistical Society, Series B, 20, 361–372.
- Graham, B.S., Campos de Xavier Pinto, C., Egel, D. (2012), Inverse Probability Tilting for Moment Condition Models With Missing Data, Review of Economic Studies, 79, 1053–1079.
- Hainmueller, J. (2012), Entropy Balancing for Causal Effects: Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies, Political Analysis, 20, 25–46.
- Hansen, L.P. (1982), Large Sample Properties of Generalized Method of Moments Estimators, Econometrica, 50, 1029–1054.
- Hirano, K., Imbens, G., Ridder, G. (2003), Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score, Econometrica, 71, 1307–1338.
- Howe, C.J., Cole, S.R., Chmiel, J.S., Muñoz, A. (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.
- Imai, K., Ratkovic, M. (2014), Covariate Balancing Propensity Score, Journal of the Royal Statistical Society, Series B, 76, 243–263.
- 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), Statistical Science, 22, 523–539.
- Lefebvre, G., Delaney, J. A.C., Platt, R.W. (2008), Impact of Mis-specification of the Treatment Model on Estimates From a Marginal Structural Model, Statistics in Medicine, 27, 3629–3642.
- Mortimer, K.M., Neugebauer, R., van der Laan, M., Tager, I.B. (2005), An Application of Model-fitting Procedures for Marginal Structural Models, American Journal of Epidemiology, 162, 382–388.
- Neyman, J. (1923), On the Application of Probability Theory to Agricultural Experiments: Essay on Principles, Section 9. (translated in 1990), Statistical Science, 5, 465–480.
- Robins, J. (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.
- (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.
- Robins, J.M., Hernán, M.A., Brumback, B. (2000), Marginal Structural Models and Causal Inference in Epidemiology, Epidemiology, 11, 550–560.
- Rubin, D.B. (1973), Matching to Remove Bias in Observational Studies, Biometrics, 29, 159–183.
- Yu, Z., van der Laan, M. (2006), Double Robust Estimation in Longitudinal Marginal Structural Models, Journal of Statistical Planning and Inference, 136, 1061–1089.