Causal bounds and instruments

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Volumn , Issue , 2007, Pages 310-317

  Ramsahai, Roland R ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX POLYTOPES; INSTRUMENTAL VARIABLES; JOINT DISTRIBUTIONS; OBSERVATIONAL DATA;

ARTIFICIAL INTELLIGENCE; ECONOMICS; INSTRUMENTS;

RANDOM VARIABLES;

EID: 77957791495     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Balke, A. and Pearl, J. (1997). Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association. 92 1171-1176.
2. Cheng, J. and Small, D.S. (2006). Bounds on causal effects in three arm trials with non-compliance. Journal of the Royal Statistical Society, Ser. B. 68 815-836. (Pubitemid 44630301)
3. Dawid, A.P. (2000). Causal inference without counterfactuals (with discussion). Journal of the American Statistical Association. 95 407-448.
4. Dawid, A.P. (2002). Influence diagrams for causal modelling and inference. International Statistical Review. 70 161-189.
5. Dawid, A. P. (2003). Causal inference using influence diagrams: The problem of partial compliance (with discussion). In Highly Structured Stochastic Systems, (ed. P.J. Green, N.L. Hjort and S. Richardson). Oxford University Press.
6. Didelez, V. and Sheehan, N.A. (2007). Mendelian randomisation as an instrumental variable approach to causal inference. Statistical Methods in Medical Research. In press.
7. Durbin, J. (1954). Errors in variables. Review of the International Statistical Institute. 22 23-32.
8. Efron, B. and Feldman, D. (1991). Compliance as an explanatory variable in clinical trials. Journal of the American Statistical Association. 86 9-26.
9. Gawrilow E. and Joswig M. (2004). Polymake web demo. Available online at URL: http://www.math.tu-berlin.de/polymake/.
10. rd International Conference on Knowledge Representation and Reasoning, (ed. B. Nebel, C. Rich and W. Swartout). Morgan Kaufmann, San Mateo, CA.
11. Imbens, G.W. and Angrist, J. (1994). Identification and estimation of local average treatment effects. Econometrica. 62 467-476.
12. Lauritzen, S.L., Dawid, A.P., Larsen, B.N. and Leimer, H.G. (1990). Independence properties of directed Markov fields. Networks. 20 491-505.
13. Lauritzen, S.L. (1996). Graphical models. Oxford University Press, UK.
14. Lauritzen, S.L. (2001). Causal inference from graphical models. In Complex Stochastic Systems, (ed. O.E. Barndorff-Nielsen, D.R. Cox, and C. Klüppelberg). CRC Press, London.
15. Lipid Research Clinic Program (1984). The lipid research clinics coronary primary prevention trial results, part I and II. Journal of the American Medical Association. 251 351-374.
16. Manski, C.F. (1990). Non-parametric bounds on treatment effects. American Economic Review, Papers and Proceedings. 80 319-323.
17. Pearl, J. (1995). Causal inference from indirect experiments. Artificial Intelligence in Medicine. 7 561-582.
18. Pearl, J. (2000). Causality: models, reasoning, and inference. Cambridge University Press, Cambridge, UK.
19. Robins, J.M. (1989). The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in logitudinal studies. In Health Service Research Methodology: A Focus on AIDS, (ed. L. Sechrest, H. Freeman and A. Mulley). U.S. Public Health Service, Washington D.C.
20. Sommer, A. and Zeger, S.L. (1991). On estimating efficacy from clinical trials. Statistics in Medicine. 10 45-52.
21. Verma, T. and Pearl, J. (1988). Causal networks: semantics and expressiveness. In Uncertainty in Artificial Intelligence, (ed. R. Shachter, T.S. Levitt and L.N. Kanal). 4 69-76. Elsevier, Amsterdam.