(Conditional) independence tests |
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(Conditional) independence tests
In this section, we would like to introduce (conditional) independence tests in causal-learn. Currently we have Fisher-z test [1], Missing-value Fisher-z test, Chi-Square test, Kernel-based conditional independence (KCI) test and independence test [2], and G-Square test [3]. Contents: Fisher-z test Missing-value Fisher-z test Chi-Square test Kernel-based conditional independence (KCI) test and independence test G-Square test [1]Fisher, R. A. (1921). On the’probable error’of a coefficient of correlation deduced from a small sample. Metron, 1, 1-32. [2]Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011, July). Kernel-based Conditional Independence Test and Application in Causal Discovery. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 804-813). AUAI Press. [3]Tsamardinos, I., Brown, L. E., & Aliferis, C. F. (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning, 65(1), 31-78. |
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