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active-bayesian-causal-inference

Active Bayesian Causal Inference (Neurips'22)

ai-projects

Artificial Intelligence projects, documentation and code.

amazon-sagemaker-hierarchical-forecasting

Hierarchical forecasting

awesome-interaction-aware-trajectory-prediction

A selection of state-of-the-art research materials on trajectory prediction

behavior_generation_lecture_python

Python Code for the Lecture "Verhaltensgenerierung für Fahrzeuge" (Behavior Generation for Vehicles) at KIT

bnt

Bayes Net Toolbox for Matlab

book

causal

Causal Inference with the package "bnlearn"

causal-inference-and-discovery-in-python

Causal Inference and Discovery in Python by Packt Publishing

causal_discovery_toolbox

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

causal_inference_julia_code

Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

causalnex

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

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

dbnr

:exclamation: This is a read-only mirror of the CRAN R package repository. dbnR — Dynamic Bayesian Network Learning and Inference. Homepage: https://github.com/dkesada/dbnR

double_ml

Intro to Double ML

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.

dyn-gfn

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks

dynamicbayesiannetworks

Code for the implementation of various methods of Non-Homogeneous Dynamic Bayesian Networks inference

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal in

expert_readed_books

2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，**类，数学类，人物传记书籍

fastai-numerical-linear-algebra

gmm-from-scratch

The only guide you need to learn everything about GMM

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

interaction-dataset

Interaction Dataset Python Scripts

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

learndatascience

Open Content for self-directed learning in data science

learning-naturalistic-driving-environment

machine-learning

methods-and-tools-for-causal-discovery-and-causal-inference

This repository is used as a support for the paper "" (to be named)