Statistical test for consistent estimation of causal effects in linear non-Gaussian models

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 364-372

  Entner, Doris ^a   Hoyer, Patrik O ^a   Spirtes, Peter ^b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIRECTED GRAPHS; ESTIMATION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); HEURISTIC ALGORITHMS; HEURISTIC METHODS; STATISTICAL TESTS;

CONSISTENT ESTIMATION; CONSISTENT ESTIMATORS; DIRECTED ACYCLIC GRAPH (DAG); GRAPH STRUCTURES; NON-GAUSSIAN MODELS; PARTIAL KNOWLEDGE; SEARCH HEURISTICS; TEMPORAL ORDER;

STATISTICS;

EID: 84946714181     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (14)

1. Darmois, G. (1953). Analyse generale des liaisons stochastiques. Revue de I'Institut International de Statistique, 21:2-8.
2. Greenland, S. (2003). Quantifying biases in causal models: Classical confounding vs collider-stratification bias. Epidemiology, 14(3):300-306.
3. Greenland, S., Pearl, J., and Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1):37-48.
4. Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Scholkopf, B., and Smola, A. J. (2008). A kernel statistical test of independence. In Advances in Neural Information Processing Systems 20.
5. Hoyer, P. O., Shimizu, S., Kerminen, A. J., and Palvi-ainen, M. (2008). Estimation of causal effects using linear non-gaussian causal models with hidden variables. International Journal of Approximate Reasoning, 49:362-378.
6. Pearl, J. (2009a). Causality: Models, Reasoning, and Inference. Cambridge University Press, 2nd edition.
7. Pearl, J. (2009b). Myth, confusion, and science in causal analysis. Technical Report R-348, University of California, Los Angeles, CA.
8. Shimizu, S., Hoyer, P. O., Hyvarinen, A., and Ker-minen, A. J. (2006). A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learing Research, 7:2003-2030.
9. Skitovitch, W. (1953). On a property of the normal distribution. Doklady Akademii Nauk SSSR, 89:217-219.
10. Spirtes, P. (2000). The limits of causal discovery from observational data. American Economic Association, Boston, MA.
11. Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. Cambridge MA: MIT Press, 2nd edition.
12. Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C. (1998). Using path diagrams as a structural equation modeling tool. Sociological Methods & Research, 27(2):182-225.
13. Tian, J., Paz, A., and Pearl, J. (1998). Finding minimal separating sets. Technical Report R-254, Computer Science Department, University of California, Los Angeles, CA.
14. Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.