Set up an object describing a causal inference problem of finding the average causal effect of some treatment on some outcome. Currently, only binary data is supported. The problem specification also allows the specification of a synthetic model, for simulation studies.
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x 
the index of the treatment variable. 
y 
the index of the outcome variable. 
latent_idx 
an array with the indices of variables which should be considered latent 
dat 
a matrix of binary data, can be ignored if a model is provided. 
g 
a binary matrix encoding a causal graph, where g[i, j] == 1 if a directed edge from vertex j to i should exist, 0 otherwise. This is only required if a ground truth model exists. 
model 
if 
num_v_max 
the maximum dimensionality in which the joint distribution implied by a model is precomputed. Having this precomputed can speed up some computations for methods that use the provided ground truth model. Because the space required to store a joint distribution grows exponentially with the dimensionality, this quantity cannot be too large. 
A cfx
object, which contains the following fields:

the index of the treatment variable in the data/graph. 

the index of the outcome variable in the data/graph. 

the array of latent variable indices given as input. 

the data given as input. 

the graph given as input. 

an array of strings with the names of the variables, as given by 

the model given as input. 

a list of arrays (if 

a multidimensional array (if 
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