Exclusion Bias in Sample-Selection Model Estimators

Avoiding model selection bias in small-sample genomic datasets

MOTIVATION Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived f...

Sample Selection Bias Correction Theory

This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error on each training point of a biased sample to more closely reflect the unbiased distribution. This relies on weights derived by various estimation techniques based on finite samples. We analyze the effe...

Models for Sample Selection Bias

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Sample selection bias in credit scoring models

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as wel...