Streamline a data analysis process
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Updated
Jun 20, 2024 - HTML
Streamline a data analysis process
R Package for Simultaneous Multi-Bias Analysis
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.
A super light-weight web app to create causal loop diagrams (CLD) online. This is useful in Systems Thinking and System Dynamics.
A Python package for modular causal inference analysis and model evaluations
A framework and specification language for simulating data based on graphical models
Artificial Intelligence Notes (causal inference)
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).
A Python library that helps data scientists to infer causation rather than observing correlation.
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
CausalVerse: An R toolkit expediting causal research & analysis. Streamlines complex methodologies, empowering users to unveil causal relationships with precision. Your go-to for insightful causality exploration.
Credici: Credal Inference for Causal Inference
Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
Desktop visual editor of causal models written in JavaScript using Electron and D3
Python package for causal discovery based on LiNGAM.
Source code for the ML models used in experiments with CA-CNN architecture.
A structure for representing possible states of a causal entity (such as plot, generalized character personality, aspects of natural language typological structure, etc.) taking into account the probabilities of facts
Simplifying audio and deep learning with PyTorch.
(Realtime) Temporal Convolutions in PyTorch
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