The three faces of faithfulness

Synthese

Volumn 193, Issue 4, 2016, Pages 1011-1027

  Zhang, Jiji ^a   Spirtes, Peter ^b  

^a LINGNAN UNIVERSITY   (Hong Kong)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Bayes nets; Causal inference; Faithfulness; Graphical models

Indexed keywords

EID: 84922615612     PISSN: 00397857     EISSN: 15730964     Source Type: Journal    
DOI: 10.1007/s11229-015-0673-9     Document Type: Article

References (22)

1. Andersen, H. (2013). When to expect violations of causal faithfulness and why it matters. Philosophy of Science Supplement, 5, 672–683.
2. Cartwright, N. (2001). What is wrong with Bayes nets? The Monist, 84, 242–264.
3. Dawid, P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B, 41, 1–31.
4. Hausman, D., & Woodward, J. (1999). Independence, invariance and the causal Markov condition. British Journal for the Philosophy of Science, 50, 521–583.
5. Hitchcock, C. (2010). Probabilistic causation. In E. Zalta (Ed.), Stanford encyclopedia of philosophy.http://plato.stanford.edu/entries/causation-probabilistic/.
6. Hoover, K. D. (2001). Causality in macroeconomics. Cambridge: Cambridge University Press.
7. Hoyer, P., Janzing, D., Mooij, J., Peters, J., Scholkopf, B. (2008). Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems 21 (NIPS 2008), pp. 689–696.
8. Kalisch, M., & Bühlmann, P. (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research, 8, 613–636.
9. Loh, P., & Bühlmann, P. (2013). High-dimensional learning of linear causal networks via inverse covariance estimation. arXiv:1311.3492v1 [stat.ML].
10. Pearl, J. (1988). Probabilistic reasoning in intelligence systems. San Mateo, CA: Morgan Kaufmann.
11. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge: Cambridge University Press.
12. Peters, J., Janzing, D., & Scholkopf, B. (2011). Causal inference on discrete data using additive noise models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2436–2450.
13. Ramsey, J., Spirtes, P., & Zhang, J. (2006). Adjacency-faithfulness and conservative causal inference. Proceedings of 22nd conference on uncertainty in artificial intelligence (UAI-06), pp. 401–408.
14. Raskutti, G., & Uhler, C. (2014). Learning directed acyclic graphs based on sparsest permutations. arXiv:1307.0366v3 [math.ST].
15. Shimizu, S., Hoyer, P., Hyvärinen, A., & Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7, 2003–2030.
16. Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search (2nd ed.). Cambridge, MA: MIT Press.
17. Spirtes, P., & Zhang, J. (2014). A uniformly consistent estimator of causal effects under the k-triangle-faithfulness assumption. Statistical Science, 29, 662–678.
18. Steel, D. (2006). Homogeneity, selection, and the faithfulness condition. Minds and Machines, 16, 303–317.
19. Woodward, J. (1998). Causal independence and faithfulness. Multivariate Behavioral Research, 33, 129–148.
20. Zhang, J., & Spirtes, P. (2008). Detection of unfaithfulness and robust causal inference’. Minds and Machines, 18(2), 239–271.
21. Zhang, J. (2013). A comparison of three Occam’s Razors for Markovian causal models. British Journal for the Philosophy of Science, 64(2), 423–448.
22. Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011). Kernel-based conditional Independence test and application in causal discovery. Proceedings of the 27th conference on uncertainty in artificial intelligence (UAI-11), pp. 804–813.