EsqYu

Thanks for your reply. I understand that very well. Speaking about LiM, can this algorithm be used to infer causal relationships of mixed variables that consist of continuous and discrete...

Thank you. I understand that LiM can be used only for mixed data of continuous variables and binary variables. What kind of approximation method are generally used to deal with...

I see. Then, what if the values of the variables are not related to the size of variables like a department code(0: Personnel 1: Sales Department ..., .etc)?

Actually, I'm talking about the test results in the tutorial. As you see in the tutorial, the true adjacency matrix is [[0. 0. ] [1.3082251 0. ]], while the estimated...

I understand. Then, is it correct that I can't use LiM just like I use LiNGAM, which can estimate both causal structures and causal effects?

Hi, thank you for your reply and introducing R package for multi scale bootstrap. Can I somehow use this package to evaluate the result LiNGAM gives?

Thanks for your reply. Does "computing bootstrap probabilities with different numbers of bootstrap resampling" mean execute the bootstrap() method with different number of n_sampling like the following? 1)model.bootstrap(X, n_sampling=100) 2)...

Thank you very much for your kind replies. I'll try using the package. By the way, I have one more question. I believe there are two options when selecting the...

Okay. Then, What is the advantage of kernel version of it ?

Thanks for your reply and sharing the URLs. I believed I should use the two methods separately depending on the features of the data used for input like skewness or...