FCM lags suspicious results about causal-learn

[kunwuz]

Thanks for the question. The optimal order (lag) for the vector regression model is determined automatically using bayesian information criterion. As a result, even if we set lags as 100, the optimal one might still be selected as 2. To avoid that optimization process, you may consider setting criterion as None, e.g.,

model = lingam.VARLiNGAM(lags=3, criterion=None)

[priamai]

What's the expected result when lags=3, is it going to permutate all the possible columns 3 times?

[kunwuz]

Sorry, I'm not sure if I fully understand the question. The lags here mean those for lagged causal relations (in contrast to contemporaneous causal relations).

[priamai]

Okay so this is my understanding so far (we should really put some basic example in the test folder):
let's say I have a classical fork DAG: X <- A -> Y.
The generation process implies that: A- lag (5) -> X and A -> lag (7) -> Y.
What's the output going to look like?
Will each edge have a different lag value?
Can we build a simple toy example to verify the desired behaviour?

[priamai]

Also would be interesting to see how to import the output into a DAG for DoWhy.