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

Last update: Nov 22, 2021

Overview

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. .

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.

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

12 May 30, 2022

[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

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

46 Dec 28, 2022

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

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

18 Nov 18, 2022

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

7 Jan 5, 2023

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

35 Nov 16, 2022

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

3k Dec 29, 2022

: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

1.8k Jan 5, 2023

Releases(revision-1.1.0)

revision-1.1.0(Jul 7, 2022)

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

Manuscript attached. Related package: https://mlconfound.readthedocs.io

Full Changelog: https://github.com/pni-lab/mlconfound-manuscript/compare/preprint-1.0.1...revision-1.1.0
Source code(tar.gz)
Source code(zip)
preprint-1.0.1(Nov 1, 2021)

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

Manuscript attached. Related package: https://mlconfound.readthedocs.io

Full Changelog: https://github.com/pni-lab/mlconfound-manuscript/compare/submit1-1.0.0...preprint-1.0.1
Source code(tar.gz)
Source code(zip)
mlconfound-arxiv.pdf(3.35 MB)
submit1-1.0.0(Oct 31, 2021)

Manuscript attached. Related package: https://mlconfound.readthedocs.io

Full Changelog: https://github.com/pni-lab/mlconfound-manuscript/compare/preprint-1.0.0...submit1-1.0.0
Source code(tar.gz)
Source code(zip)
mlconfound-submit.pdf(3.37 MB)
preprint-1.0.0(Oct 30, 2021)

T. Spisak, Statistical quantification of confounding bias in predictive modelling, a preprint, 2021.

Manuscript attached. Related package: https://mlconfound.readthedocs.io
Source code(tar.gz)
Source code(zip)
mlconfound-arxiv.pdf(3.35 MB)

Owner

PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany

GitHub Repository https://mlconfound.readthedocs.io

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images In this paper, we present an effective Dynamic Enhancement Anchor

13 Dec 09, 2022

The code uses SegFormer for Semantic Segmentation on Drone Dataset.

SegFormer_Segmentation The code uses SegFormer for Semantic Segmentation on Drone Dataset. The details for the SegFormer can be obtained from the foll

1 May 08, 2022

Federated Learning Based on Dynamic Regularization

Federated Learning Based on Dynamic Regularization This is implementation of Federated Learning Based on Dynamic Regularization. Requirements Please i

39 Jan 07, 2023

PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

Value Iteration Networks in PyTorch Tamar, A., Wu, Y., Thomas, G., Levine, S., and Abbeel, P. Value Iteration Networks. Neural Information Processing

75 Nov 24, 2022

Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

21 May 18, 2022

Code release for ConvNeXt model

A ConvNet for the 2020s Official PyTorch implementation of ConvNeXt, from the following paper: A ConvNet for the 2020s. arXiv 2022. Zhuang Liu, Hanzi

4.6k Jan 08, 2023

这个开源项目主要是对经典的时间序列预测算法论文进行复现，模型主要参考自GluonTS，框架主要参考自Informer

Time Series Research with Torch 这个开源项目主要是对经典的时间序列预测算法论文进行复现，模型主要参考自GluonTS，框架主要参考自Informer。建立原因相较于mxnet和TF，Torch框架中的神经网络层需要提前指定输入维度： # 建立线性层 TensorF

85 Dec 29, 2022

Codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks

DominoSearch This is repository for codes and models of NeurIPS2021 paper - DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense n

11 Sep 10, 2022

A tf.keras implementation of Facebook AI's MadGrad optimization algorithm

MADGRAD Optimization Algorithm For Tensorflow This package implements the MadGrad Algorithm proposed in Adaptivity without Compromise: A Momentumized,

20 Aug 18, 2022

Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

Box_Discretization_Network This repository is built on the pytorch [maskrcnn_benchmark]. The method is the foundation of our ReCTs-competition method

266 Nov 24, 2022

Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

22 Nov 30, 2022

Implementation of paper "DeepTag: A General Framework for Fiducial Marker Design and Detection"

Implementation of paper DeepTag: A General Framework for Fiducial Marker Design and Detection. Project page: https://herohuyongtao.github.io/research/

46 Dec 12, 2022

Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

17 Jan 01, 2023