causal-discovery 0.1.0

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Causal discovery

Causal discovery is the process of inferring causal relationships between variables from observational data. This repository aims to provide a collection of causal discovery algorithms implemented in Python.

Development setup

This repository uses Poetry as a dependency manager. To install the dependencies, run:

$ poetry install

Usage

Pull this repository and run the following command:

$ poetry build

Then, install the package:

$ pip install dist/causal-discovery-0.1.0.tar.gz

example usage:

from causal_discovery.algos.notears import NoTears

# load dataset
dataset = ...  

# initialize model
model = NoTears(
    rho=1, 
    alpha=0.1, 
    l1_reg=0, 
    lr=1e-2
)

# learn the graph
_ = model.learn(dataset.X)

# adjacency matrix
print(model.W)

Algorithms

Algorithm	Reference
NOTEARS	DAGs with NO TEARS: Continuous Optimization for Structure Learning, 2019

Results

This is the example of the results of the algorithm.

How to read this figure: for any value in coordinate (x, y) means the causal effect from y -> x.