3 papers
stat.ML2026
Debiased Front-Door Learners for Heterogeneous Effects
Yonghan Jung
In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal e…
stat.ML2026
Information-Theoretic Causal Bounds under Unmeasured Confounding
Yonghan Jung, Bogyeong Kang
We develop a data-driven information-theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrict…
cs.LG2026
Path-specific effects for pulse-oximetry guided decisions in critical care
Kevin Zhang, Yonghan Jung, Divyat Mahajan +2
Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate p…