DGCIT: Double Generative Adversarial Networks for Conditional Independence Testing (JMLR, 2021)
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Updated
Jan 19, 2022 - Python
DGCIT: Double Generative Adversarial Networks for Conditional Independence Testing (JMLR, 2021)
Bayesian Network applied to the context of italian Fantasy Football for Fundamentals of Artificial Intelligence and Knowledge Representation class at @unibo
Learning non-Gaussian graphical models
Tools for analyzing and quantifying effects of confounder variables on machine learning model predictions.
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.
Package for analyzing GWAS summary statistics data
Variable Selection with Knockoffs
An algorithm to create a bayesian network and check independance of the variables
Flow-based PC algorithm for causal discovery using Normalizing Flows
Code for the paper "Causal Domain Adaptation with Copula Entropy based Conditional Independence Test"
implementation of fair dummies
(Conditional) Independence testing & Markov blanket feature selection using k-NN mutual information and conditional mutual information estimators. Supports continuous, discrete, and mixed data, as well as multiprocessing.
The Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT)
Code for the paper "Estimating Transfer Entropy via Copula Entropy"
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