Cross-classifications by joint potential outcomes

Causality: Wiley Series in Probability and Statistics

Volumn , Issue , 2012, Pages 114-125

  Sjölander, Arvid ^a  

^a KAROLINSKA INSTITUTE   (Sweden)

Author keywords

'Principal stratification', special cross classification by joint potential outcomes; Complier causal effect identification, and assumption of monotonicity; Complier causal effect in randomized trials, with imperfect compliance; Complier causal effect, on consistency assumption, exchangeability, exclusion restriction; Cross classifications by joint potential outcomes; Observational studies, causal effects in general, as not identifiable; Standard analyses for randomized trials with imperfect compliance, ITT and AT; Unmeasured confounding, using IV techniques

Indexed keywords

EID: 84957092556     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781119945710.ch10     Document Type: Chapter

References (29)

1. Angrist, J.D. and Imbens G.W. (1991) Source of identifying information in evaluation models. Discussion Paper 1568, Department of Economics, Harvard University, Cambridge, MA.
2. Angrist, J.D., Imbens, G.W. and Rubin, D.B. (1996) Identification of causal effects using instrumental variables. Journal of the American Statistical Asssociation, 91 (434), 444-455.
3. Balke, A. and Pearl, J. (1994) Counterfactual probabilities: computational methods, bounds, and applications. Technical Report R-213-B, UCLA Cognitive Systems Laboratory.
4. Balke, A. and Pearl, J. (1997) Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association, 92, 1171-1176.
5. Bloom, H.S. (1984) Accounting for no-shows in experimental evaluation designs. Evaluation Review, 8, 225-246.
6. Cox D.R. and Hinkley, D.V. (1974) Theoretical Statistics. London: Chapman and Hall.
7. Dawid, A.P. (2000) Causal inference without counterfactuals (with discussion). Journal of the American Statistical Association, 95 (450), 407-448.
8. Dawid, A.P. (2003) Causal inference using influence diagrams: the problem of partial compliance (with discussion), in Highly Structured Stochastic Systems (eds PJ. Green, N.L. Hjort and S. Richardson). Oxford University Press, pp. 45-81.
9. Frangakis, C.E. and Rubin, D.B. (2002) Principal stratification in causal inference. Biometrics, 58,21-29.
10. Gilbert, P.B. and Bosch, J.B. and Hudgens, M.G. (2003) Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials. Biometrics, 59, 531-541.
11. Hernan, M.A. and Robins, J.M. (2006) Instruments for causal inference: an epidemiologists dream? Epidemiology, 17 (4), 360-372.
12. Imbens, G.W. and Angrist, J.D. (1994) Identification and estimation of local average treatement effects. Econometrica, 62 (2), 467-475.
13. Imbens, G.W. and Rubin, D.B. (1997) Bayesian inference for causal effects in randomized experiments with noncompliance. Annals of Statistics, 25, 305-327.
14. Jemiai, Y. (2005) Semiparametric methods for the effect of treatment on an outcome existing only in a post-randomization selected subpopulation. PhD Thesis, Harvard University, Cambridge, MA.
15. Jemiai, Y., Rotnitzky, A., Shepherd, B.E. and Gilbert, P.B. (2007) Semiparametric estimation of treatment effects on an outcome measured after a post-randomization event occurs. Journal of the Royal Statistical Society, 69, 879-901.
16. Kalbfleish, J.D. and Prentice, R.L. (1980) The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons, Inc.
17. Manski, C.F. (1990) Nonparametric bounds on treatment effects. American Economic Review, Papers and Proceedings, 80, 319-323.
18. Manski, C.F. (2007) Identification for Prediction and Decision. Cambridge, MA: Harvard University Press.
19. Pearl, J. (1993) Aspects of graphical models connected to causality, in Proceedings of the 49th Session of the International Statistical Institute, Italy, Tome LV, Book 1, Florence, pp. 399-401.
20. Pearl, J. (1995a) Causal inference from indirect experiments. Artificial Intelligence in Medicine, 7, 561-582.
21. Pearl, J. (1995b) Causal diagrams for empirical research. Biometrika, 82 (4), 669-710.
22. Pearl, J. (2000) Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.
23. Robins, J.M. (1986) A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Mathematical Modelling, 7, 1393-1512.
24. Robins, J.M. (1989) The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies, in Health Service Research Methodology: A focus on AIDS (eds L. Sechrest, H. Freeman and A. Mulley). Washington, DC: US Public Health Service, National Center for Health Services Research, pp. 113-159.
25. Robins, J.M. and Greenland, S. (1992) Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.
26. Shepherd, B.E., Gilbert, P.B., Jemiai, Y. and Rotnitzky, A. (2006) Sensitivity analysis comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials. Biometrics, 62, 332-342.
27. Sjolander, A. (2009) Bounds on natural direct effects in the presence of confounded intermediate variables. Statistics in Medicine, 28 (4), 558-571.
28. Vansteelandt, S. and Goethgebeur, E. (2005) Sense and sensitivity when correcting for observed exposures in randomized clinical trials. Statistics in Medicine, 24, 191-210.
29. Zhang, J.L. and Rubin, D.B. (2003) Estimation of causal effects via principal stratification when some outcomes are truncated by 'death'. Journal of Educational and Behavioral Statistics, 28(4), 353-368.