cyclicmodel 0.0.4

statistical causality discovery based on cyclic model

cyclicmodel

Statistical causal discovery based on cyclic model.
This project is under development.

Summary

Python package that performs statistical causal discovery under the following condition:

1. there are unobserved common factors
2. two-way causal relationship exists

cyclicmodel has been developed based on bmlingam, which implemented bayesian mixed LiNGAM.

Example

import numpy as np
import pymc3 as pm
import cyclicmodel as cym

# Generate synthetic data,
# which assumes causal relation from x1 to x2
n = 200
x1 = np.random.randn(n)
x2 = x1 + np.random.uniform(low=-0.5, high=0.5, size=n)
xs = np.vstack([x1, x2]).T

# Model settings
hyper_params = cym.define_model.CyclicModelParams(
    dist_std_noise='log_normal',
    df_indvdl=8.0,
    dist_l_cov_21='uniform, -0.9, 0.9',
    dist_scale_indvdl='uniform, 0.1, 1.0',
    dist_beta_noise='uniform, 0.5, 6.0')

# Generate PyMC3 model
model = cym.define_model.get_pm3_model(xs, hyper_params, verbose=10)

# Run variational inference with PyMC3
with model:
  fit = pm.FullRankADVI().fit(n=100000)
  trace = fit.sample(1000, include_transformed=True)

# Check the posterior mean of the coefficients
print(np.mean(trace['b_21']))  # from x1 to x2
print(np.mean(trace['b_12']))  # from x2 to x1

Installation

pip install cyclicmodel

References

• LiNGAM - Discovery of non-gaussian linear causal models
• Shimizu, S., & Bollen, K. (2014). Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15(1), 2629-2652.