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Python package for (conditional) independence testing and statistical functions related to causality.
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Experimental library integrating LLM capabilities to support causal analyses
[Experimental] Global causal discovery algorithms
[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
Making it easier to build stable, trustworthy data-science pipelines based on the PCS framework.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its go…
Data Analysis with Bootstrapped ESTimation
Hyper-parameter optimization for sklearn
Distributed Asynchronous Hyperparameter Optimization in Python
Sequential model-based optimization with a `scipy.optimize` interface
The machine learning toolkit for time series analysis in Python
Bayesian Analysis with Python (Second Edition)
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
A community based Python library for quantitative economics
An intuitive library to add plotting functionality to scikit-learn objects.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.