Description Usage Arguments Value
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.
1 2 |
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 pre-computed. Having this pre-computed 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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