Next generation of automated data exploratory analysis and visualization platform.

Python Toolkit for Causal and Probabilistic Reasoning

Causal Discovery in Python. Learning causality from data.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Trustworthy AI related projects

An Awesome List of the latest time series papers and code from top AI venues.

Open-Source Network Management, ITOM, and Security Analytics platform

Must-read papers and resources related to causal inference and machine (deep) learning

Python package for causal discovery based on LiNGAM.

YLearn, a pun of "learn why", is a python package for causal inference

Tutorials on Causal Inference and pgmpy

A resource list for causality in statistics, data science and physics

Causal discovery algorithms and tools for implementing new ones

StatsPAI is the first agent-native Python library for causal inference and applied econometrics — unified API, broad cross-method coverage, structured result objects, machine-readable schemas, an MCP server, and R/Stata parity validation.

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

(ICLR 2022) Discovering Invariant Rationales for Graph Neural Networks

DreamGraph is a graph-governed conceptual development environment (CDE) that turns plans, architecture decisions, and project knowledge into auditable execution through a persistent cognitive graph.

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"