Results 1 to 10 of about 4,525,334 (379)
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic [PDF]
Web Search and Data Mining, 2021 User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect to enjoy beforehand (positivity bias). Methods exist for mitigating the effects of selection bias in user ratings on the evaluation and optimization of RSs.
Jin Huang +2 more
arxiv +3 more sources
Debiased Graph Neural Networks with Agnostic Label Selection Bias [PDF]
arXiv, 2022 Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set. In reality, the test data is not even available during the training process, making selection bias agnostic.
Shaohua Fan +5 more
arxiv +3 more sources
Selection bias due to conditioning on a collider.
BMJ, 2023 Effect estimates may be biased when the study design or the data analysis is conditional on a collider—a variable that is caused by two other variables. Causal directed acyclic graphs are a helpful tool to identify colliders that may introduce selection ...
Hernán MA, Monge S.
europepmc +2 more sources
Sensitivity analysis of selection bias: a graphical display by bias-correction index [PDF]
PeerJ, 2023 Background In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction ...
Ping-Chen Chung, I-Feng Lin
doaj +3 more sources
A potential outcomes approach to selection bias [PDF]
Epidemiology 34(6): 865-872, November 2023, 2020 We propose a novel definition of selection bias in analytic epidemiology using potential outcomes. This definition captures selection bias under both the structural approach (where conditioning on selection into the study opens a noncausal path from exposure to disease in a directed acyclic graph) and the traditional definition (where a given measure ...
Kenah E.
arxiv +2 more sources
Contextualizing selection bias in Mendelian randomization: how bad is it likely to be? [PDF]
arXiv, 2018 Selection bias affects Mendelian randomization investigations when selection into the study sample depends on a collider between the genetic variant and confounders of the risk factor-outcome association. However, the relative importance of selection bias for Mendelian randomization compared to other potential biases is unclear.
A. Gkatzionis, S. Burgess
arxiv +3 more sources
Correcting for Selection Bias in Learning-to-rank Systems [PDF]
The Web Conference, 2020 Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems.
Zohreh Ovaisi +4 more
semanticscholar +1 more source
Applications of propensity score matching: a case series of articles published in [PDF]
Annals of Coloproctology, 2022 Propensity score matching (PSM) is an increasingly applied method of ensuring comparability between groups of interest. However, PSM is often applied unconditionally, without precise considerations.
Hwa Jung Kim
doaj +1 more source
Toward a clearer definition of selection bias when estimating causal effects.
Epidemiology, 2022 Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing ...
Haidong Lu +3 more
semanticscholar +1 more source
Analyzing the impact of missing values and selection bias on fairness
International Journal of Data Science and Analysis, 2021 Algorithmic decision making is becoming more prevalent, increasingly impacting people’s daily lives. Recently, discussions have been emerging about the fairness of decisions made by machines.
Yanchen Wang, L. Singh
semanticscholar +1 more source