Simplifying Bias Correction for Selective Sampling: A Unified Distribution-Free Approach to Handling Endogenously Selected Samples
提出一种无分布假设的方法，在最小化建模假设和分析负担的同时，利用信息丰富的内生选择样本并减少样本选择偏差。
Exploiting informative endogenously selected samples while minimizing sample selection bias with minimum modeling assumptions and analytical burden.