Skip to main content

statistical causality discovery based on cyclic model

Project description

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cyclicmodel, version 0.0.4
Filename, size File type Python version Upload date Hashes
Filename, size cyclicmodel-0.0.4-py3-none-any.whl (5.6 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size cyclicmodel-0.0.4.tar.gz (5.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page