Popular GitHub Repositories for Causal Models

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

A list of Graph Causal Learning materials.

Python package for the creation, manipulation, and learning of Causal DAGs

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

Causal Inference & Deep Learning, MIT IAP 2018

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

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

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

A python package for finding causal functional connectivity from neural time series observations.

Code library for training causal inference deep learning models with automatic hyperparameter optimization written in Tensorflow 2.

This repository focuses on advancing the process of causal graph generation by integrating the capabilities of Large Language Models (LLMs) and time-tested algorithms from causal theory.

This is the implementation of low rank adaptation (LoRA) which is a subset of parameter efficient fine tuning (PEFT).