An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
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Updated
Jun 9, 2020 - R
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
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 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.
Causal Deconvolution of Networks by Algorithmic Generative Models
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.
dosearch: R Package for Identifying General Causal Queries
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.
Shiny app illustrating the movie star example based on S. Cunningham "Causal Inference: The Mixtape" (Section 3.1.6)
Tools for sensitivity analysis for weighted estimators
A FOSS course cultivating the skills and understanding necessary to bring value to organisations by improving decision-making
Tool to extract causal relationships from biological and medical databases that are in tabular format
An R package for learning context-specific causal models, called CStrees, based on observational, or a mix of observational and interventional, data.
'A brief introduction to causal assumptions in statistical models' presentation for the James Cook University CodeR group based in Townsville and Cairns, Australia.
Causal Discovery between Manufacturer-Retailer price channels
Shiny app illustrating the cholesterol example for the Simpson's paradox in Glymour et al. (2016, Section 1.2)
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