cyclicmodel 0.0.1

statistical causality discovery based on cyclic model

Copyright (c) 2018 Akimitsu INOUE and Shohei SHIMIZU

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Description: # cyclicmodel
Statistical causal discovery based on cyclic model

## 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`][4670f282], which implemented bayesian mixed LiNGAM.

[4670f282]: https://github.com/taku-y/bmlingam "bmlingam"

## Example
```Python
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
```bash
pip install
```

## References
- [LiNGAM - Discovery of non-gaussian linear causal models](https://sites.google.com/site/sshimizu06/lingam)
- [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.](http://jmlr.org/papers/volume15/shimizu14a/shimizu14a.pdf)

Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/markdown