Causai
Causai is a Python library for doing Causality in Machine Learning. We provide state-of-the-art causal & ML/DL algorithms into decision-making systems.
Why Causai?
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Main Features
Get Started
Installation
pip install causai
or
git clone https://github.com/DanielhCarranza/causai.git
cd causai
Example
import causai
from causai.datasets import load_datasets
from causai.inference import CausalInference
from causai.discovery import AutoDiscovery
Codebase
causai
datasets
: Logic for downloading, preprocessing, augmenting, and loading datamodels
: Models wrap networks and add functionality like loss functions. saving, loading, and trainingnetworks
: Code for constructing ML model, neural net or bayesian net (dumb input | output mappings)tests
: Regression tests for the models code. Make sure a trained model performs well on important examples.metrics
estimator
predictor.py
: wrapper for model that allows you to do inference in the apiutils.py
notebooks
: Examples, Tutorials, Explore and visualize data
tasks
: Scripts for running frequent tests and training commands
training
: Logic for the training itself
api
: Serve predictions. (Contains DockerFiles, Unit Tests, Flask, etc.)
evaluation
: Run the validation tests
experiment_manager
: Settings of your experiment manager (p.e. wandb, tensorboard)
data
: use it for data versioning, storing data examples and metadata of your datasets. During training use it to store your raw and processed data but don't push or save the datasets into the repo.