Frontiers of Mathematics in China >
Covariate selection for identifying the effects of a particular type of conditional plan using causal networks
Received date: 08 Feb 2010
Accepted date: 07 May 2010
Published date: 05 Dec 2010
Copyright
Suppose that cause-effect relationships between variables can be described by a causal network with a linear structural equation model. Kuroki and Miyakawa proposed a graphical criterion for selecting covariates to identify the effect of a conditional plan with one control variable [J. Roy. Statist. Soc. Ser. B, 2003, 65: 209-222]. In this paper, we study a particular type of conditional plan with more than one control variable and propose a graphical criterion for selecting covariates to identify the effect of a conditional plan of the studied type.
Na SHAN , Jianhua GUO . Covariate selection for identifying the effects of a particular type of conditional plan using causal networks[J]. Frontiers of Mathematics in China, 2010 , 5(4) : 687 -700 . DOI: 10.1007/s11464-010-0064-y
| 1 |
Anderson T W. An Introduction to Multivariate Statistical Analysis. 2nd ed. New York: Wiley, 1984
|
| 2 |
Balke A, Pearl J. Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association, 1997, 92: 1171-1176
|
| 3 |
Cochran W G. The omission or addition of an independent variable in multiple linear regression. Supplement to Journal of the Royal Statistical Society, 1938, 5: 171-176
|
| 4 |
Geiger D, Verma T S, Pearl J. Identifying independence in Bayesian networks. Networks, 1990, 20: 507-534
|
| 5 |
Greenland S, Pearl J, Robins J M. Causal diagrams for epidemiologic research. Epidemiology, 1999, 10: 37-48
|
| 6 |
Huang Y, Valtorta M. Identifiability in causal bayesian networks: A sound and complete algorithm. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2006, 1149-1154
|
| 7 |
Kuroki M, Miyakawa M. Covariate selection for estimating the causal effect of control plans by using causal diagrams. Journal of the Royal Statistical Society Series B, 2003, 65: 209-222
|
| 8 |
Pearl J. Probabilistic Reasoning Intelligent Systems. San Francisco: Morgan Kaufmann, 1988
|
| 9 |
Pearl J. Causal diagrams for empirical research (with discussion). Biometrika, 1995, 83: 669-710
|
| 10 |
Pearl J. Graphs, causality and structural equation models. Sociological Methods and Research, 1998, 27: 226-284
|
| 11 |
Pearl J. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press, 2000
|
| 12 |
Pearl J, Robins J. Probabilistic evaluation of sequential plans from causal models with hidden variables. In: Besnard P, Hanks S, eds. Uncertainty in Artificial Intelligence, 11. San Francisco: Morgan Kaufmann, 1995, 669-709
|
| 13 |
Shpitser I, Pearl J. Identification of joint interventional distributions in recursive semi-Markovian causal models. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2006, 1219-1226
|
| 14 |
Spirtes P, Glymour C, Scheines R. Causation, Prediction and Search. New York: Springer, 1993
|
| 15 |
Tian J. Identifying dynamic sequential plans. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI). Helsinki: AUAI Press, 2008, 554-561
|
| 16 |
Tian J, Pearl J. On the identification of causal effects, technical Report R-290-L. Department of Computer Science, University of California, Los Angeles, 2003
|
| 17 |
Wermuth N. Moderating effects in multivariate normal distributions. Methodika, 1989, 3: 74-93
|
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