causal-models

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.

Must-read papers and resources related to causal inference and machine (deep) learning

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

Causal Inference & Deep Learning, MIT IAP 2018

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

A resource list for causality in statistics, data science and physics

Uplift modeling and evaluation library. Actively maintained pypi version.

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

CAusal Reasoning for Network Identification with integer VALue programming in R

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

Initial look at directed acyclic graph (DAG) based causal models in regression.

causaleffect: R package for identifying causal effects.

A Brief Overview of Causal Inference (xaringan presentation)

causalMGM is an R package that allow users to learn undirected and directed (causal) graphs over mixed data types (i.e., continuous and discrete variables).

Uses several statistical tests / algorithms on marginal / conditional distributions

Graphical Hypergeometric Networks

Causal Inference Using Quasi-Experimental Methods

Credici: Credal Inference for Causal Inference

Code and figures for the Differential Causal Inference (DCI) algorithm

This repository contains the CEO ontology, the evaluation corpus and the CEO vocabulary.

Cyclic Causal Inference

How to make common social science diagrams using DiagrammeR

The Semantic Web Expert System Shell - An intelligent system platform capable of reasoning from multiple ontologies using Resource Description Framework (RDF), Rule Markup Language (RuleML), as well as other knowledge expressed as functional, structural, or causal models.

Friendly introduction to causal inference

Causality reading group

bayesian networks made easy

This is a project by Asmir Muminovic and Lukas Kolbe, which was created for the Applied Predictive Analytics class held by the Chair of Information Systems at the Humboldt University of Berlin

Code accompanying my 2021 ASA SDSS paper