causy 0.0.10

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causy

Causal discovery made easy.

Installation

Currently we only support python 3.11. To install causy run

pip install causy

Usage

Causy can be used via CLI or via code.

Usage via CLI

Run causy with one of the default algorithms

causy execute --help
causy execute your_data.json --algorithm PC --output-file output.json

The data you can use is a json file with a list of dictionaries. Each dictionary represents a data point. The keys of the dictionary are the variable names and the values are the values of the variables. The values can be either numeric or categorical.

[
    {"a": 1, "b": 0.3},
    {"a": 0.5, "b": 0.2}
]

You can customize your causy pipeline by ejecting and modifying the pipeline file.

causy eject PC pc.json
# edit pc.json
causy execute tests/fixtures/toy_data_larger.json --pipeline pc.json

This might be useful if you want to use a custom algorithm or if you want to customize the pipeline of a default algorithm.

Usage via Code

Use a default algorithm

from causy.algorithms import PC
from causy.utils import retrieve_edges

model = PC()
model.create_graph_from_data(
    [
        {"a": 1, "b": 0.3},
        {"a": 0.5, "b": 0.2}
    ]
)
model.create_all_possible_edges()
model.execute_pipeline_steps()
edges = retrieve_edges(model.graph)

for edge in edges:
    print(
        f"{edge[0].name} -> {edge[1].name}: {model.graph.edges[edge[0]][edge[1]]}"
    )

Use a custom algorithm

from causy.exit_conditions import ExitOnNoActions
from causy.graph import graph_model_factory, Loop
from causy.independence_tests import (
    CalculateCorrelations,
    CorrelationCoefficientTest,
    PartialCorrelationTest,
    ExtendedPartialCorrelationTestMatrix,
)
from causy.orientation_tests import (
    ColliderTest,
    NonColliderTest,
    FurtherOrientTripleTest,
    OrientQuadrupleTest,
    FurtherOrientQuadrupleTest,
)
from causy.utils import retrieve_edges

CustomPC = graph_model_factory(
    pipeline_steps=[
        CalculateCorrelations(),
        CorrelationCoefficientTest(threshold=0.1),
        PartialCorrelationTest(threshold=0.01),
        ExtendedPartialCorrelationTestMatrix(threshold=0.01),
        ColliderTest(),
        Loop(
            pipeline_steps=[
                NonColliderTest(),
                FurtherOrientTripleTest(),
                OrientQuadrupleTest(),
                FurtherOrientQuadrupleTest(),
            ],
            exit_condition=ExitOnNoActions(),
        ),
    ]
)

model = CustomPC()

model.create_graph_from_data(
    [
        {"a": 1, "b": 0.3},
        {"a": 0.5, "b": 0.2}
    ]
)
model.create_all_possible_edges()
model.execute_pipeline_steps()
edges = retrieve_edges(model.graph)

for edge in edges:
    print(
        f"{edge[0].name} -> {edge[1].name}: {model.graph.edges[edge[0]][edge[1]]}"
    )

Supported algorithms

Currently causy supports the following algorithms:

• PC (Peter-Clark)
  • PC - the original PC algorithm without any modifications causy.algorithms.PC
  • ParallelPC - a parallelized version of the PC algorithm causy.algorithms.ParallelPC

Supported pipeline steps

Detailed information about the pipeline steps can be found in the API Documentation.

Dev usage

Setup

We recommend using poetry to manage the dependencies. To install poetry follow the instructions on https://python-poetry.org/docs/#installation.

Install dependencies

poetry install

Execute tests

poetry run python -m unittest discover -s tests