Search Packages

causallib

A Python package for modular causal inference analysis and model evaluations

pgmpy

Python Toolkit for Causal and Probabilistic Reasoning

dowhy

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.

lingam

Python package for causal discovery based on LiNGAM.

bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

pytorch-tcn

(Realtime) Temporal Convolutions in PyTorch

cdt

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

syntheticcontrolmethods

A Python package for causal inference using Synthetic Controls

pylift

Uplift modeling and evaluation library. Actively maintained pypi version.

pqp

Subroutines for structural causal modeling

ylearn

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

tfce-mediation

Fast regression and mediation analysis of vertex or voxel MRI data with TFCE

esa-2scm

ESA-2SCM Python Package for Causal Discovery

torch-utilities

Simplifying audio and deep learning with PyTorch.

scmopy

scmopy: Distribution-Agnostic Structural Causal Models Optimization in Python

dagsim

A framework and specification language for simulating data based on graphical models

actualcauses

A package for Actual Causes Identification

tmi-viewer

tmi_viewer

jupyterlab-dagitty

JupyterLab renderer of dagitty causal diagrams

cause2e

A package for end-to-end causal analysis

pgmpy-rcr

Python Toolkit for Causal and Probabilistic Reasoning

iscan-dag

Implementation of the iSCAN algorithm for detecting distribution shifts

causationentropy

Implementation of Causation Entropy from Clarkson Center for Complex Systems Science (C3S2)

causalflows

CausalFlows: A library for Causal Normalizing Flows in Pytorch