Causal Deconvolution of Networks by Algorithmic Generative Models
-
Updated
May 9, 2019 - R
Causal Deconvolution of Networks by Algorithmic Generative Models
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
The simMixedDAG package enables simulation of "real life" datasets from DAGs
Estimates the inference of a Fuzzy Cognitive Map (FCM). Provides a selection of 6 different inference rules and 4 threshold functions in order to obtain the inference of the FCM.
The orientDAG package is used to orient DAG edges. It also includes utility functions to convert DAGs between different representations as well as measure DAG dissimilarity measures.
Code for the paper "Causal Domain Adaptation with Copula Entropy based Conditional Independence Test"
Provides some functions for calculating causal effects.
An R script to perform two sample Mendelian randomization screening (with TwoSampleMR) for a custom summary statistic against a set of summary statistics from the IEU GWAS database.
Tool to extract causal relationships from biological and medical databases that are in tabular format
R code for causal graph animations
Supplementary Materials for ``Quantitative Social Science: An Introduction''
Tools for sensitivity analysis for weighted estimators
An R package for learning context-specific causal models, called CStrees, based on observational, or a mix of observational and interventional, data.
R companion to Angrist Pischke Mostly Harmless econometrics
Add a description, image, and links to the causality topic page so that developers can more easily learn about it.
To associate your repository with the causality topic, visit your repo's landing page and select "manage topics."