23 Open source projects that are alternatives of or similar to pycid

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.

[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Code for our paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

An implementation of Bayesian Networks Model for pure C++14 (11) later, including probability inference and structure learning method.

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context

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

Regulatory networks with Direct Information in python

A Causal Relation Schema for Text

Causal Deconvolution of Networks by Algorithmic Generative Models

Causal Discovery for Python. Translation and extension of the Tetrad Java code.

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

We will keep updating the paper list about machine learning + causal theory. We also internally discuss related papers between NExT++ (NUS) and LDS (USTC) by week.

Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断，神经逻辑，强人工智能逻辑推理前沿领域。

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

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

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

Implementation of Invariant Risk Minimization https://arxiv.org/abs/1907.02893

Machine learning based causal inference/uplift in Python

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Python Library for learning (Structure and Parameter) and inference (Probabilistic and Causal) in Bayesian Networks.

🎲 A Kotlin DSL for probabilistic programming.

Software for learning sparse Bayesian networks