statistical causality discovery based on cyclic model
Statistical causal discovery based on cyclic model.
This project is under development.
Python package that performs statistical causal discovery under the following condition:
- there are unobserved common factors
- two-way causal relationship exists
cyclicmodel has been developed based on
bmlingam, which implemented bayesian mixed LiNGAM.
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
pip install cyclicmodel
- 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.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|cyclicmodel-0.0.4-py3-none-any.whl (5.6 kB) Copy SHA256 hash SHA256||Wheel||py3||Jun 14, 2018|
|cyclicmodel-0.0.4.tar.gz (5.4 kB) Copy SHA256 hash SHA256||Source||None||Jun 14, 2018|