Prior Knowledge Module not Registering all Forbidden Variables

[cschiri]

Regards,

I am using pycausal in:

• Anaconda's distribution of Python 3.7.

• Windows 8.1

Everything is working superbly. However, I am having issues with the Prior Knowledge module.

My dataframe has:

• Two independent variables named "y1" and "y2". There is no causal relationship between the variables, and, temporally, they both precede the dependent variables that are listed below;

• 19 dependent variables named "v1" to "v19". While they are interesting, at this stage I do not seek to discover the interrelationships between these dependent variables. I am only interested in their relationships to "y1" and "y2".

I fed this prior knowledge into pycausal as follows:

# Prior Knowledge

from pycausal import prior as pk

forbidden = [['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7',
              'v8', 'v9', 'v10', 'v11', 'v12', 'v13, 'v14',
              'v15', 'v16', 'v17', 'v18', 'v19']]

tempForbid = pk.ForbiddenWithin(['y1', 'y2'])

temporal = [tempForbid, ['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7',
              'v8', 'v9', 'v10', 'v11', 'v12', 'v13, 'v14',
              'v15', 'v16', 'v17', 'v18', 'v19']]

prior = pk.knowledge(forbiddirect = forbidden, addtemporal = temporal)
prior

However, not all, i.e. just two (of), the variables are being registered as the "forbiddirect" section of the following output shows:

Instance of edu.cmu.tetrad.data.Knowledge2: /knowledge
addtemporal

1* y1 y2
2  v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v13 v14 v15 v16 v17 v18 v19

forbiddirect
v1 v2

requiredirect

Can you advise if there is anything wrong with the approach I am taking to code in prior knowledge?

Everything else works well, the subsequent parts my search, causal graph output etc work beautifully.

However, I just don't want to waste valuable time computing causal relationships (between "v#" variables) I am not interested in (my dataset is fairly big and I am using the bayesian search).

[chirayukong]

Could you help? @jdramsey