CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
AI summary
Causal Inference Tool
A comprehensive Python package for causal inference and graph structure recovery from observational data
- stars
- 1.1K
- forks
- 199
- watching
- 37
Similar projects
Found by comparing what the projects do, not just their names.
Causal analysis tool
A package for computing asymmetric Shapley values to assess causality in machine learning models
Time series inference library
An R package for estimating the effect of an intervention on time series data
Causality resource
A collection of resources and code snippets for understanding causal relationships in various fields.
RDF Type Inference Tool
An application for statistical type inference on RDF datasets in N3/N-Quads format
Causal modeler
Provides a Python package for structural causal modeling of multivariate time series data
Causality tracker
A framework for tracking causality between events in real-time
DAG creator
A package for creating and analyzing causal directed acyclic graphs using a consistent and easy-to-use API
Forensic analysis toolkit
A Python-based collection of tools for gathering forensic information from Office documents
Graph robustness toolkit
A toolbox for graph reliability and robustness against noise, distribution shifts, and attacks.
Causality tracker
A data structure to track the causal history of values in a distributed key-value store
Causal dataset
A dataset and annotation scheme for Hungarian causal reasoning tasks.
Causal Estimator
An R implementation of a machine learning approach to estimate the effect of treatment on individuals
Graph analytics library
A package providing graph statistical algorithms and utilities for processing and analyzing graphs
Connectomics toolkit
A deep learning framework for automatic and semi-automatic segmentation of 3D image stacks in connectomics
Deep learning toolkit
A Python framework for building deep learning models with optimized encoding layers and batch normalization.
