Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.

Related tags

Deep Learningmachine-learningstatistical-testspermutation-testconditional-independenceconfounder
Overview

arXiv lintex

Git repositoty of the manuscript entitled

Statistical quantification of confounding bias in predictive modelling

by Tamas Spisak

The manuscript describes and validates the package mlconfound.

Read the docs. Documentation Status.

Abstract

The lack of non-parametric statistical tests for confounding bias significantly hampers the development of robust, valid and generalizable predictive models in many fields of research. Here I propose the partial and full confounder tests, which, for a given confounder variable, probe the null hypotheses of unconfounded and fully confounded models, respectively.

The tests provide a strict control for Type I errors and high statistical power, even for non-normally and non-linearly dependent predictions, often seen in machine learning. Applying the proposed tests on models trained on functional brain connectivity data from the Human Connectome Project and the Autism Brain Imaging Data Exchange dataset reveals confounders that were previously unreported or found to be hard to correct for with state-of-the-art confound mitigation approaches.

The tests (implemented in the package mlconfound can aid the assessment and improvement of the generalizability and neurobiological validity of predictive models and, thereby, foster the development of clinically useful machine learning biomarkers.

This repository contains:

  • The latex source of the manuscript describing the 'mlconfound' approach: see manuscript.tex and related files.
  • Sll source code required to reproduce the results in the manuscript. See the directories: simulated and empirical.
  • All results. See the directories simulated/results and the analysis notebooks.
  • All figures. See the directory fig.

To reproduce the whole analysis:

./reproduce.sh

Citation

T. Spisak, Statistical quantification of confounding bias in predictive modelling, preprint on arXiv:2111.00814, 2021.

Licensing

  • Manuscript source and figures (contents of the root folder and the fig dir): CC BY
  • Source code (contents of the empirical and simulated folders): GPL3

Acknowledgements

The manuscript builds on an aesthetic and simple LaTeX style suitable for "preprint" publications such as arXiv and bio-arXiv, etc. It is based on the nips_2018.sty style.

You might also like...
PyTorch Code of "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics"

Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati

Yang Li 12 May 30, 2022
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias

Counterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in C

Yulei Niu 94 Dec 3, 2022
A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK

Pytorch-MBNet A pytorch implementation of MBNET: MOS PREDICTION FOR SYNTHESIZED SPEECH WITH MEAN-BIAS NETWORK Training To train a new model, please ru

null 46 Dec 28, 2022
Submission to Twitter's algorithmic bias bounty challenge
Submission to Twitter's algorithmic bias bounty challenge

Twitter Ethics Challenge: Pixel Perfect Submission to Twitter's algorithmic bias bounty challenge, by Travis Hoppe (@metasemantic). Abstract We build

Travis Hoppe 4 Aug 19, 2022
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022
Implementation for
Implementation for "Domain-Specific Bias Filtering for Single Labeled Domain Generalization"

DSBF Introduction This repository contains the implementation code for paper: Domain-Specific Bias Filtering for Single Labeled Domain Generalization

ScottYuan 7 Jan 5, 2023
This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.
This is our ARTS test set, an enriched test set to probe Aspect Robustness of ABSA.

This is the repository for our 2020 paper "Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis". Data We provide

null 35 Nov 16, 2022
Fast, flexible and easy to use probabilistic modelling in Python.
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

Achilles Rasquinha 1.8k Jan 5, 2023
Releases(revision-1.1.0)
Owner
PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany
PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany
GitHub Repository https://mlconfound.readthedocs.io
A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation.

TiSASRec.paddle A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation. Introduction 论文：Time Interval Aware Sel

Paddorch 2 Nov 28, 2021
Graph parsing approach to structured sentiment analysis.

Fine-grained Sentiment Analysis as Dependency Graph Parsing This repository contains the code and datasets described in following paper: Fine-grained

Jeremy Barnes 36 Dec 12, 2022
StyleMapGAN - Official PyTorch Implementation

StyleMapGAN - Official PyTorch Implementation StyleMapGAN: Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing Hyunsu Kim, Yunj

NAVER AI 425 Dec 23, 2022
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AIST

Marcelo Hartmann 2 May 06, 2022
Nest - A flexible tool for building and sharing deep learning modules

Nest - A flexible tool for building and sharing deep learning modules Nest is a flexible deep learning module manager, which aims at encouraging code

ZhouYanzhao 41 Oct 10, 2022
GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data

GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data By Shuchang Zhou, Taihong Xiao, Yi Yang, Dieqiao Feng, Qinyao He, W

Taihong Xiao 141 Apr 16, 2021
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System This repository contains code for the paper Schultheis,

2 Oct 28, 2022
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Website | ArXiv | Get Start | Video PIRenderer The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic

Ren Yurui 261 Jan 09, 2023
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

34 Oct 08, 2022
ParaGen is a PyTorch deep learning framework for parallel sequence generation

ParaGen is a PyTorch deep learning framework for parallel sequence generation. Apart from sequence generation, ParaGen also enhances various NLP tasks, including sequence-level classification, extrac

Bytedance Inc. 169 Dec 22, 2022
Pytorch implementation for RelTransformer

RelTransformer Our Architecture This is a Pytorch implementation for RelTransformer The implementation for Evaluating on VG200 can be found here Requi

Vision CAIR Research Group, KAUST 21 Nov 22, 2022
Evaluation suite for large-scale language models.

This repo contains code for running the evaluations and reproducing the results from the Jurassic-1 Technical Paper (see blog post), with current support for running the tasks through both the AI21 S

71 Dec 17, 2022
Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

Introduction This repository contains my unofficial reimplementation of the standard ECAPA-TDNN, which is the speaker recognition in VoxCeleb2 dataset

Tao Ruijie 277 Dec 31, 2022
Unit-Convertor - Unit Convertor Built With Python

Python Unit Converter This project can convert Weigth,length and ... units for y

Mahdis Esmaeelian 1 May 31, 2022
TransGAN: Two Transformers Can Make One Strong GAN

[Preprint] "TransGAN: Two Transformers Can Make One Strong GAN", Yifan Jiang, Shiyu Chang, Zhangyang Wang

VITA 1.5k Jan 07, 2023
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

40 Dec 13, 2022
Code repository for Self-supervised Structure-sensitive Learning, CVPR'17

Self-supervised Structure-sensitive Learning (SSL) Ke Gong, Xiaodan Liang, Xiaohui Shen, Liang Lin, "Look into Person: Self-supervised Structure-sensi

Clay Gong 219 Dec 29, 2022
Simple object detection app with streamlit

object-detection-app Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see

Robin Cole 68 Jan 02, 2023
A basic neural network for image segmentation.

Unet_erythema_detection A basic neural network for image segmentation. 前期准备 1.在logs文件夹中下载h5权重文件，百度网盘链接在logs文件夹中 2.将所有原图 放置在“/dataset_1/JPEGImages/”文件夹

1 Jan 16, 2022
FastFace: Lightweight Face Detection Framework

Light Face Detection using PyTorch Lightning

Ömer BORHAN 75 Dec 05, 2022