Papers Selection bias
“Selection bias” 태그가 달린 논문 365편 · 필터 해제
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparoscopic and open surgical techniques in a co…
Causal InferenceSelection biasSurvival AnalysisLLM-Generated Feedback Supports Learning If Learners Choose to Use It
Large language models (LLMs) are increasingly used to generate feedback, yet their impact on learning remains underexplored, especially compared to existing feedback methods. This study investigates how on-demand LLM-gen…
Selection biasAddressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
Recommendation systems (RS) aim to provide personalized content, but they face a challenge in unbiased learning due to selection bias, where users only interact with items they prefer. This bias leads to a distorted repr…
FairnessImputationRecommendation SystemsSelection biasUnderstanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal performance across subgroups is an unrelia…
FairnessSelection biasRecover Experimental Data with Selection Bias using Counterfactual Logic
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased…
Causal InferencecounterfactualSelection biasSmart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
We study an extension of contextual stochastic linear optimization (CSLO) that, in contrast to most of the existing literature, involves inequality constraints that depend on uncertain parameters predicted by a machine l…
Conformal PredictionSelection biasIce Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference
Reliable causal inference is essential for making decisions in high-stakes areas like medicine, economics, and public policy. However, it remains unclear whether large language models (LLMs) can handle rigorous and trust…
BenchmarkingCausal InferenceSelection biasRepresentation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
Estimating treatment effects from observational data is challenging due to two main reasons: (a) hidden confounding, and (b) covariate mismatch (control and treatment groups not having identical distributions). Long line…
Representation LearningSelection biasvalidOn the Value of Cross-Modal Misalignment in Multimodal Representation Learning
Multimodal representation learning, exemplified by multimodal contrastive learning (MMCL) using image-text pairs, aims to learn powerful representations by aligning cues across modalities. This approach relies on the cor…
Contrastive LearningRepresentation LearningSelection biasCan SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond
We revisit the problem of estimating $k$ linear regressors with self-selection bias in $d$ dimensions with the maximum selection criterion, as introduced by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [CDIZ23, STOC…
Selection biasRegression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
We consider the problem of estimating the expected causal effect $E[Y|do(X)]$ for a target variable $Y$ when treatment $X$ is set by intervention, focusing on continuous random variables. In settings without selection bi…
regressionSelection biasWhen Selection Meets Intervention: Additional Complexities in Causal Discovery
We address the common yet often-overlooked selection bias in interventional studies, where subjects are selectively enrolled into experiments. For instance, participants in a drug trial are usually patients of the releva…
Causal DiscoverycounterfactualSelection biasDeterminants of the Spousal Age Gap in India: Analysis of Indian Microdata
This study examines the determinants of the spousal age gap (SAG) in India, utilizing data from the 61st and 68th rounds of the National Sample Survey (NSSO). We employ regression analysis, including instrumental variabl…
Selection biasChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typical CVR positive sample usually goes thro…
counterfactualRecommendation SystemsSelection biasGene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data, providing insights into regulatory mechanisms. A significant yet often overlooked challenge is select…
Selection biasInstrumental Variables with Time-Varying Exposure: New Estimates of Revascularization Effects on Quality of Life
The ISCHEMIA Trial randomly assigned patients with ischemic heart disease to an invasive treatment strategy centered on revascularization with a control group assigned non-invasive medical therapy. As is common in such `…
Selection biasCherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim s…
Selection biasTime SeriesTime Series ForecastingEntire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction
In recommender systems, post-click conversion rate (CVR) estimation is an essential task to model user preferences for items and estimate the value of recommendations. Sample selection bias (SSB) and data sparsity (DS) a…
Knowledge DistillationRecommendation SystemsSelection biasEVOS: Efficient Implicit Neural Training via EVOlutionary Selector
We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each it…
Evolutionary AlgorithmsSelection biasEGEAN: An Exposure-Guided Embedding Alignment Network for Post-Click Conversion Estimation
Accurate post-click conversion rate (CVR) estimation is crucial for online advertising systems. Despite significant advances in causal approaches designed to address the Sample Selection Bias problem, CVR estimation stil…
Selection bias