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Causal Polytree ---Pearl's classical algorithm(1988)

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Pearl's famous causal polytree recover algorithm is implemented here.



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As a famous sub-structure of Bayesian network, causal polytree is able to recover the causality very efficiently.

Here, I implement pearl's classical algorithm here for easy using. Details can be seen in Pearl's paper[1].

To recover general Causal polytree, one can download "Fisher's exact test" in my space for conditional independence test.

One can start from ControlCenter.m, I add a simple example there for better understanding.

If there is any question, just let me know, I will response to you as soon as possible.

[1] G. Rebane, J. Pearl, The recovery of causal poly-trees from statistical data, in: Proceedings of the Third Conference on Uncertainty Artificial Intelligence, Seattle, Washington, 1987, pp. 222–228

Comments and Ratings (4)

Guangdi Li

Guangdi Li (view profile)

The function " view(biograph( A ))"
demonstrates the matrix A in the form of nice graph. Indeed, you need that toolbox to visually check the result, instead of checking the produced matrix manually.

Liviu Vladutu

Your example it relies on a script biograph from Computational Biology tbox....

Guangdi Li

Guangdi Li (view profile)

sorry about it , I have updated it.

Sebastien PARIS

IsLeaf function undefined ....



update the file "IsLeaf.m", sorry about my carelessness.


update the graph

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