The Difference Causal Inference (DCI) algorithm directly learns the difference between two causal graphs given two datasets. DCI is implemented as a part of the causaldag package. The source code for DCI can be found in the package here.
Install
To install the causaldag package:
$ pip3 install causaldag
Simple Example
from causaldag import dci, dci_stability_selection
from causaldag.datasets import create_synthetic_difference
import numpy as np
import itertools as itr
X1, X2, true_difference = create_synthetic_difference(nnodes=8, nsamples=10000)
p = X1.shape[1]
difference_matrix = dci(X1, X2, difference_ug=list(itr.combinations(range(p), 2)))
ddag_edges = set(zip(*np.where(difference_matrix != 0)))
print(true_difference)
print(ddag_edges)
Tutorial
DCI is applied to gene expression data collected from two different conditions to learn the causal difference gene regulatory network.