Lab Sessions - Causal Data Science for Business Analytics (Summer Term 2024)
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Updated
Jul 9, 2024 - HTML
Lab Sessions - Causal Data Science for Business Analytics (Summer Term 2024)
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 graphical models and potential outcomes frameworks.
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Pytorch implementation of 'Explaining text classifiers with counterfactual representations' (Lemberger & Saillenfest, 2024)
[Experimental] Global causal discovery algorithms
Python package for causal discovery based on LiNGAM.
Causal discovery made easy.
The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML
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 goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal …
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
DecoR: Deconfounding Time Series with Robust Regression
Official PyTorch Implementation for "Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection" in CVPR 2024
A toolbox for integrated information theory.
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
Next generation of automated data exploratory analysis and visualization platform.
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
Tools for sensitivity analysis for weighted estimators
YLearn, a pun of "learn why", is a python package for causal inference
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