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Selection bias

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://bitbucket.org/mvdschaar/mlforhealthlabpub

공식 코드

Learning Overlapping Representations for the Estimation of Individualized Treatment Effects

https://github.com/2dot71mily/sib_paper

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Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

https://github.com/2dot71mily/spurious_correlations_icml_2022

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Selection Bias Induced Spurious Correlations in Large Language Models

https://github.com/2dot71mily/uspec

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Underspecification in Language Modeling Tasks: A Causality-Informed Study of Gendered Pronoun Resolution

https://github.com/A-bone1/ESAM

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ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

https://github.com/CSHaitao/CalibraEval

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CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges

https://github.com/DanielNgo207/Incentivizing-Compliance-with-Algorithmic-Instruments

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Incentivizing Compliance with Algorithmic Instruments

https://github.com/FabianObster/sgboost-introduction

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Sparse-Group Boosting with Balanced Selection Frequencies: A Simulation-Based Approach and R Implementation

https://github.com/H-shw/futurepedia

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Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation

https://github.com/HarrieO/2020ictir-evaluation

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Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

https://github.com/HarrieO/2021wsdm-unifying-LTR

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Unifying Online and Counterfactual Learning to Rank

https://github.com/HarrieO/OnlineLearningToRank

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To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions

https://github.com/IntelLabs/causality-lab

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Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias

https://github.com/Jo-Lam/Jyutping_project

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Decolonising Data Systems: Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage

https://github.com/KimYenny/HeterogeneousRF

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Heterogeneous Random Forest

https://github.com/MarkDana/CDIS

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When Selection Meets Intervention: Additional Complexities in Causal Discovery

https://github.com/Powertony102/corn_official

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Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation

https://github.com/RyanWangZf/SurvTRACE

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SurvTRACE: Transformers for Survival Analysis with Competing Events

https://github.com/TIA-Lab/PanNuke-metrics

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PanNuke Dataset Extension, Insights and Baselines

https://github.com/TURuibo/Neuropathic-Pain-Diagnosis-Simulator

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Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

https://github.com/alishiraligit/eval-on-laion

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What Makes ImageNet Look Unlike LAION

https://github.com/amazon-science/multi-way-parallel-ccmatrix

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A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism

https://github.com/ambroiseodt/tsim

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Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias

https://github.com/arthua196/Leakage-Neutral-Learning-for-QuoraQP

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Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

https://github.com/askhanatgithub/DRI_ITE

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On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement

https://github.com/baowenxuan/boba

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BOBA: Byzantine-Robust Federated Learning with Label Skewness

https://github.com/berryxue/PIF

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Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors

https://github.com/betsyhj/dancer

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It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic

https://github.com/betsyhj/multifactorialbias

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Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

https://github.com/bnn-upc/graphlaxy

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Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

https://github.com/chen-gx/cie

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Causality and Independence Enhancement for Biased Node Classification

https://github.com/christopher-br/cbrnet

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Using representation balancing to learn conditional-average dose responses from clustered data

https://github.com/chujiezheng/llm-mcq-bias

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Large Language Models Are Not Robust Multiple Choice Selectors

https://github.com/clinicalml/rct-obs-extrapolation

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Falsification before Extrapolation in Causal Effect Estimation

https://github.com/congjiang/tnddr

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A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Québec Administrative Data

https://github.com/conradborchers/ai-feedback-exp

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LLM-Generated Feedback Supports Learning If Learners Choose to Use It

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