Warning
causy is currently in a very early and experimental stage of development. It currently only supports one algorithm. We do not recommend using it in production.
Causal discovery made easy. Causy allows you to use and implement causal discovery algorithms with easy to use, extend and maintain pipelines. It is built based on pytorch which allows you to run the algorithms on CPUs as well as GPUs seamlessly.
Current causal discovery algorithms are often designed for the primary purpose of research. They are often implemented in a monolithic way, which makes it hard to understand and extend them. Causy aims to solve this problem by providing a framework which allows you to easily implement and use causal discovery algorithms by splitting them up into smaller logic steps which can be stacked together to form a pipeline. This allows you to easily understand, extend, optimize, and experiment with the algorithms.
By shipping causy with sensitively configured default pipelines, we also aim to provide a tool that can be used by non-experts to get started with causal discovery.
Thanks to the pytorch backend, causy is remarkably faster compared to serial CPU based implementations.
In the future, causy aims to provide interactive visualizations which allow you to understand the causal discovery process.
Currently we only support python 3.11. To install causy run
pip install causy
Causy can be used via CLI or via code.
Run causy with one of the default algorithms
causy execute --help
causy execute your_data.json --algorithm PC --output-file output.json
The input data should be 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 your_data.json --pipeline pc.json --output-file output.json
This might be useful if you want to configure a custom algorithm or if you want to customize the pipeline of a default algorithm.
To visualize the causal discovery process, we are currently working on a web-based UI. The UI is currently in a very early stage of development and is not yet ready for use. If you want to try it out, you can run the following command:
causy ui output.json
This runs a web server on port 8000 which allows you to visualize the causal discovery process. The UI is currently read-only and does not allow you to interact with the causal discovery process.
Use a default algorithm
from causy.causal_discovery.constraint.algorithms import PC
from causy.graph_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.common_pipeline_steps.exit_conditions import ExitOnNoActions
from causy.graph_model import graph_model_factory
from causy.common_pipeline_steps.logic import Loop
from causy.common_pipeline_steps.calculation import CalculatePearsonCorrelations
from causy.causal_discovery.constraint.independence_tests.common import (
CorrelationCoefficientTest,
PartialCorrelationTest,
ExtendedPartialCorrelationTestMatrix,
)
from causy.causal_discovery.constraint.orientation_rules.pc import (
ColliderTest,
NonColliderTest,
FurtherOrientTripleTest,
OrientQuadrupleTest,
FurtherOrientQuadrupleTest,
)
from causy.models import Algorithm
from causy.causal_discovery.constraint.algorithms.pc import PC_EDGE_TYPES
from causy.graph_utils import retrieve_edges
CustomPC = graph_model_factory(
Algorithm(
pipeline_steps=[
CalculatePearsonCorrelations(),
CorrelationCoefficientTest(threshold=0.05),
PartialCorrelationTest(threshold=0.05),
ExtendedPartialCorrelationTestMatrix(threshold=0.05),
ColliderTest(),
Loop(
pipeline_steps=[
NonColliderTest(),
FurtherOrientTripleTest(),
OrientQuadrupleTest(),
FurtherOrientQuadrupleTest(),
],
exit_condition=ExitOnNoActions(),
),
],
name="CustomPC",
edge_types=PC_EDGE_TYPES,
)
)
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]]}"
)
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
- PCStable - a stable version of the PC algorithm
causy.algorithms.PCStable
- PC - the original PC algorithm without any modifications
Detailed information about the pipeline steps can be found in the API Documentation.
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
Funded by Prototype Fund from March 2024 until September 2024