2026
Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
ICML 2026poster
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 i…