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 Python library that helps data scientists to infer causation rather than observing correlation.

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

A Python package for modular causal inference analysis and model evaluations

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

Python package for causal discovery based on LiNGAM.

A Python package for causal inference using Synthetic Controls

This repository contains the dataset and the PyTorch implementations of the models from the paper Recognizing Emotion Cause in Conversations.

(Realtime) Temporal Convolutions in PyTorch

The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"

Uplift modeling and evaluation library. Actively maintained pypi version.

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Scalable open-source software to run, develop, and benchmark causal discovery algorithms

A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.

The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).

Code for the Structural Agnostic Model (https://arxiv.org/abs/1803.04929)

A spatiotemporal causal convolutional network for predicting PM2.5 concentrations.

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

Create soft prompts for fairseq 13B dense, GPT-J-6B and GPT-Neo-2.7B for free in a Google Colab TPU instance

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