ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2017
The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- An Assessment of Racial Disparities in Pretrial Decision-Making Using Misclassification Models.Journal of empirical legal studies · 2026Article
- Advancing evaluation of AI systems when humans make the decisions.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Building Nondiscriminatory Algorithms in Selected Data.American economic review. Insights · 2025Article
- Recovery Potential in Patients After Cardiac Arrest Who Die After Limitations or Withdrawal of Life Support.JAMA network open · 2025Article
- From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.Proceedings of machine learning research · 2024Article
- Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing.Quantitative marketing and economics · 2024Article
- Gender equity in hiring: examining the effectiveness of a personality-based algorithm.Frontiers in psychology · 2023Article
- From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices.Science and engineering ethics · 2020Article
- Predicting high-risk opioid prescriptions before they are given.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Article
- HUMAN DECISIONS AND MACHINE PREDICTIONS.The quarterly journal of economics · 2018Article
- Introduction to the Special Collection on the Fragile Families Challenge.Socius : sociological research for a dynamic worldArticle
- Preventing rather than punishing: An early warning model of malfeasance in public procurement.International journal of forecastingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.