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Robins (1998) introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding.
Rubin, D.B., 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5), p.688
1974
Earlier work this paper cites.
Rosenbaum, P.R. and Rubin, D.B., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), pp.41-55
1983
Earlier work this paper cites.
Holland, P.W., 1986. Statistics and causal inference. Journal of the American statistical Association, 81(396), pp.945-960
1986
Earlier work this paper cites.
Robins, J., 1986. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical modelling, 7(9-12), pp.1393-1512
1986
Earlier work this paper cites.
Robins J. A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. Journal of chronic diseases. 1987 Jan 1;40:139S-61S
1987
Earlier work this paper cites.
Newey, W.K. and McFadden, D., 1994. Large sample estimation and hypothesis testing. Handbook of econometrics, 4, pp.2111-2245
1994
Cited alongside, same era.
Robins J.M. (1998). Marginal structural models. In: 1997 Proceedings of the Section on Bayesian Statistical Science, Alexandria, VA: American Statistical Association, 1998;1-10
1998
Cited alongside, same era.
Robins, J. M. ”Association, causation, and marginal structural models.” Synthese 121, no. 1-2 (1999): 151-179
1999
Cited alongside, same era.
Robins, J.M., Greenland, S. and Hu, F.C., 1999. Estimation of the causal effect of a time-varying exposure on the marginal mean of a repeated binary outcome. Journal of the American Statistical Association, 94(447), pp.687-700
1999
Cited alongside, same era.
Robins, J.M., 2000a. Marginal structural models versus structural nested models as tools for causal inference. In Statistical models in epidemiology, the environment, and clinical trials (pp. 95-133). Springer, New York, NY
Robins, J.M., 2000b. Robust estimation in sequentially ignorable missing data and causal inference models. In Proceedings of the American Statistical Association (Vol. 1999, pp. 6-10)
1999
Later among the works it cites.
Hernán, M.Á., Brumback, B. and Robins, J.M., 2000. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology, pp.561-570
2000
Later among the works it cites.
Robins, J.M., Rotnitzky, A. and Scharfstein, D.O., 2000. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In Statistical models in epidemiology, the environment, and clinical trials (pp. 1-94). Springer, New York, NY
2000
Later among the works it cites.
Robins, J.M., Hernan, M.A. and Brumback, B., 2000. Marginal structural models and causal inference in epidemiology
2000
Later among the works it cites.
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Wang, L. and Tchetgen Tchetgen, E., (2018a). Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(3), pp.531-550
Cited in the paper.