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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://github.com/lguerdan/cp_ome

공식 코드

Counterfactual Prediction Under Outcome Measurement Error

https://github.com/lodino/crab

공식 코드

Consistent Range Approximation for Fair Predictive Modeling

https://github.com/longjp/causal-bias-code

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Sample Selection Bias in Evaluation of Prediction Performance of Causal Models

https://github.com/med-air/top-gpm

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Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data

https://github.com/mfederici/dsit

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An Information-theoretic Approach to Distribution Shifts

https://github.com/mkuzma96/cg-ct

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Causal Machine Learning for Cost-Effective Allocation of Development Aid

https://github.com/nanguoshun/IVG

공식 코드

Interventional Video Grounding with Dual Contrastive Learning

https://github.com/niftynans/matched_CFR

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Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects

https://github.com/nkny/bandwagonictir2022

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The Bandwagon Effect: Not Just Another Bias

https://github.com/ntudsp/singapore-soundscape-site-selection-survey

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Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering

https://github.com/octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference

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Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

https://github.com/peteryang1031/causal-gwib

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Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck

https://github.com/philipboeken/debiased_regression

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Correcting for Selection Bias and Missing Response in Regression using Privileged Information

https://github.com/philipversteeg/sbcd

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Local Constraint-Based Causal Discovery under Selection Bias

https://github.com/recflow-iclr/recflow

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RecFlow: An Industrial Full Flow Recommendation Dataset

https://github.com/rfgong/IVaps

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Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules

https://github.com/riiid/useraif

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Addressing Selection Bias in Computerized Adaptive Testing: A User-Wise Aggregate Influence Function Approach

https://github.com/sharif-ml-lab/GABInsight

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GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models

https://github.com/shashankg7/bias-preference-elicitation

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A First Look at Selection Bias in Preference Elicitation for Recommendation

https://github.com/shifwang/paper-debiased-feature-importance

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A Debiased MDI Feature Importance Measure for Random Forests

https://github.com/shirara1016/si_for_cpd_by_rnn

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Selective Inference for Changepoint detection by Recurrent Neural Network

https://github.com/snfrieder/ghosts

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Mathematical Capabilities of ChatGPT

https://github.com/songjinbo/ECMM

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Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models

https://github.com/sunxin000/uncertainty4recsys

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Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation

https://github.com/superpig99/SBRL-HAP

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Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations

https://github.com/tagas/bQCD

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Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery

https://github.com/tanganke/hmr

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Improving Heterogeneous Model Reuse by Density Estimation

https://github.com/thomassutter/mvhg

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Learning Group Importance using the Differentiable Hypergeometric Distribution

https://github.com/thu-bpm/gradlre

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Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

https://github.com/tydpan/gpc

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Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

https://github.com/usaito/asymmetric-tri-rec-real

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Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback

https://github.com/usaito/ijcai2022-adversarial-mf

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Towards Resolving Propensity Contradiction in Offline Recommender Learning

https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/causal_multitask_gaussian_processes_ite

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Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

https://github.com/vila-lab/open-llm-leaderboard

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Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena

https://github.com/vsemenova/leebounds

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Generalized Lee Bounds

https://github.com/vshirvaikar/recidivism

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Rethinking recidivism through a causal lens

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