Towards a holistic understanding of Selection Bias for Causal Effect Identification 事件

Towards a holistic understanding of Selection Bias for Causal Effect Identification arXiv:2605.13430v2 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher socio-economic status than the population they are meant to represent. Recovering causal effects from such sub-population is an important problem in causal inference, as estimating