ICPy 0.0.6

Invariant Causal Prediction for python

ICPy

This packages provides a simple python implementation of Invariant Causal Prediction (ICP) [1].
The source code for the actual algorithm resides in ./src/icp/ICP.py.
See also the original implementation in the R package InvariantCausalPrediction.

Installation

pip install ICPy

Usage

import icpy as icpy
import numpy as np

np.random.seed(seed=1)
n = 100
noise = 0.1
E = np.repeat([0, 1, 2], np.ceil(n / 3.0))[0:n]                       # "Environment"
A = np.random.normal(scale=noise, size=[n]) + np.equal(E, 1)          # Node A
B = A + np.random.normal(scale=noise, size=[n]) / 3 + np.equal(E, 2)  # Node B
C = B + np.random.normal(scale=noise, size=[n])                       # Node C

#  /--->---\
# E -> A -> B -> C

icpy.invariant_causal_prediction(X=np.column_stack((A, B)), y=C, z=E) # test if A or B are parents of C

Output

ICP(S_hat=array([1], dtype=int64),                         # Column 1 = Node B was (correctly) identified as parent of C
    p_values=array([  1.51508232e-01,   4.59577055e-37]),  # error levels at which A and B would/are indentied as parent of C
    p_value=0.16416488336322549)                           # p-value for testing against violation of the model assumptions (e.g. a direct effect of E on C)

News

v0.0.003 (2020-05-15)

• fix failing import (thanks to @lgmoneda, #1)
• fix issues when environments are not subsequent whole numbers starting at 0 (thanks to @lgmoneda, #1)

References

[1] J. Peters, P. Bühlmann, N. Meinshausen, Causal inference by using invariant prediction: identification and confidence intervals, J. R. Stat. Soc. Ser. B Stat. Methodol. 78 (2016) 947-1012. doi:10.1111/rssb.12167.