Signed directed acyclic graphs for causal inference

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Volumn 72, Issue 1, 2010, Pages 111-127

  Vanderweele, Tyler J ^a   Robins, James M ^a  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Bias; Causal inference; Confounding; Directed acyclic graphs; Structural equations

Indexed keywords

EID: 74049158300     PISSN: 13697412     EISSN: 14679868     Source Type: Journal    
DOI: 10.1111/j.1467-9868.2009.00728.x     Document Type: Article

References (37)

1. Archer, N. P. Wang, S. (1993) Application of the backpropagation neural network algorithm with monotonicity constraints for two-group classification problems. Decsn Sci., 24, 60 75.
2. Bioch, J. C. (1998) Dualization, decision lists and identification of monotone discrete functions. Ann. Math. Artif. Intell., 13, 69 91.
3. Cox, D. R. Wermuth, N. (2003) A general condition for avoiding effect reversal after marginalization. J. R. Statist. Soc. B, 65, 937 941.
4. Dawid, A. P. (2000) Causal inference without counterfactuals. J. Am. Statist. Ass., 95, 407 424.
5. Dawid, A. P. (2002) Influence diagrams for causal modelling and inference. Int. Statist. Rev., 70, 161 189.
6. Druzdzel, M. J. Henrion, M. (1993) Efficient reasoning in qualitative probabilistic networks. In Proc. 11th A. Conf. Artificial Intelligence, pp. 548 553. Menlo Park : American Association for Artificial Intelligence Press.
7. Duncan, O. D. (1975) Introduction to Structural Equation Models. New York : Academic Press.
8. Esary, J. D., Proschan, F. Walkup, D. W. (1967) Association of random variables, with applications. Ann. Math. Statist., 38, 1466 1474.
9. van der Gaag, L. C., Bodlaender, H. L Feelders, A. (2004) Monotonicity in Bayesian networks. In Uncertainty in Artificial Intelligence (eds M. Chickering J. Halpern vol. 20, pp. 569 576. San Francisco : Morgan Kaufmann.
10. Galles, D. Pearl, J. (1995) Testing identifiability of causal effects. In Uncertainty in Artificial Intelligence (eds P. Besnard S. Hanks vol. 11, pp. 185 195. San Francisco : Morgan Kaufmann.
11. Geiger, D., Verma, T. S. Pearl, J. (1990) Identifying independence in bayesian networks. Networks, 20, 507 534.
12. Geng, Z., Guo, J. Fung, W.-K. (2002) Criteria for confounders in epidemiological studies. J. R. Statist. Soc. B, 64, 3 15.
13. Greenland, S., Pearl, J. Robins, J. M. (1999) Causal diagrams for epidemiologic research. Epidemiology, 10, 37 48.
14. Haavelmo, T. (1943) The statistical implications of a system of simultaneous equations. Econometrica, 11, 1 12.
15. Kuroki, M. Miyakawa, M. (1999) Identifiability criteria for causal effects of joint interventions. J. Jpn Statist. Soc., 29, 105 117.
16. Kuroki, M. Miyakawa, M. (2003) Covariate selection for estimating the causal effect of control plans by using causal diagrams. J. R. Statist. Soc. B, 65, 209 222.
17. Lauritzen, S. L., Dawid, A. P., Larsen, B. N. Leimer, H. G. (1990) Independence properties of directed Markov fields. Networks, 20, 491 505.
18. Lauritzen, S. L. Richardson, T. S. (2002) Chain graph models and their causal interpretations (with discussion). J. R. Statist. Soc. B, 64, 321 361.
19. Pearl, J. (1993) Comment: Graphical models, causality, and intervention. Statist. Sci., 8, 266 269.
20. Pearl, J. (1995) Causal diagrams for empirical research. Biometrika, 82, 669 688.
21. Pearl, J. (2000) Causality: Models, Reasoning, and Inference. Cambridge : Cambridge University Press.
22. Pearl, J. Robins, J. M. (1995) Probabilistic evaluation of sequential plans from causal models with hidden variables. In Uncertainty in Artificial Intelligence (eds P. Besnard S. Hanks vol. 11, pp. 444 453. San Francisco : Morgan Kaufmann.
23. Potharst, R. Feelders, A. (2002) Classification trees for problems with monotonicity constraints. In Proc. Special Interest Group on Knowledge Discovery and Data Mining Explorations, vol. 4, pp. 1 10. New York : Association for Computing Machinery.
24. Robins, J. M. (1986) A new approach to causal inference in mortality studies with sustained exposure period - application to control of the healthy worker survivor effect. Math. Modllng, 7, 1393 1512.
25. Robins, J. M. (1987) Addendum to a new approach to causal inference in mortality studies with sustained exposure period - application to control of the healthy worker survivor effect. Comput. Math. Applic., 14, 923 945.
26. Robins, J. M. (1995). Discussion of "Causal diagrams for empirical research" by J. Pearl. Biometrika, 82, 695 698.
27. Robins, J. M. (1997) Causal inference from complex longitudinal data. Lect. Notes Statist., 120, 69 117.
28. Robins, J. M. (2003) Semantics of causal DAG models and the identification of direct and indirect effects. In Highly Structured Stochastic Systems (eds P. Green, N. L. Hjort S. Richardson pp. 70 81. New York : Oxford University Press.
29. Spirtes, P., Glymour, C. Scheines, R. (1993) Causation, Prediction and Search. New York : Springer.
30. Tian, J. Pearl, J. (2002) On the identification of causal effects. In Proc. 18th Natn. Conf. Artificial Intelligence,. 567 573. Menlo Park : American Association for Artificial Intelligence Press.
31. VanderWeele, T. J., Hernán, M. A. Robins, J. M. (2008) Causal directed acyclic graphs and the direction of unmeasured confounding bias. Epidemiology, 19, 720 728.
32. VanderWeele, T. J. Robins, J. M. (2007) Directed acyclic graphs, sufficient causes and the properties of conditioning on a common effect. Am. J. Epidem., 166, 1096 1104.
33. VanderWeele, T. J. Robins, J. M. (2009a) Properties of monotonic effects on directed acyclic graphs. J. Mach. Learn. Res., 10, 699 718.
34. VanderWeele, T. J. Robins, J. M. (2009b) Minimal sufficient causation and directed acyclic graphs. Ann. Statist., 37, 1437 1465.
35. Verma, T. Pearl, J. (1990) Causal networks: semantics and expressiveness. In Uncertainty in Artificial Intelligence (eds R. Shachter, T. S. Levitt L. N. Kanal vol. 4,. 69 76. Amsterdam : Elesevier.
36. Wellman, M. P. (1990) Fundamental concepts of qualitative probabilistic networks. Artif. Intell., 44, 257 303.
37. Wright, S. (1921) Correlation and causation. J. Agric. Res., 20, 557 585.