5 papers
Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
Yunfeng Wang, Zhiheng Zhang, Zijun Gao
Partial identification provides informative causal guarantees when point identification is impossible, but existing approaches based on optimal transport (OT) become computationall…
Statistical Inference in Causal Partial Identification with Smooth Densities
Sirui Lin, Zijun Gao, Jose Blanchet +1
Many causal quantities are only partially identifiable due to the inherent missingness of potential outcomes, and the associated partial identification (PI) sets can be obtained by…
Causal Partial Identification via Conditional Optimal Transport
Sirui Lin, Zijun Gao, Jose Blanchet +1
We study the estimation of causal estimand involving the joint distribution of treatment and control outcomes for a single unit. In typical causal inference settings, it is impossi…
Tightening Causal Bounds via Covariate-Aware Optimal Transport
Sirui Lin, Zijun Gao, Jose Blanchet +1
Causal estimands can vary significantly depending on the relationship between outcomes in treatment and control groups, potentially leading to wide partial identification (PI) inte…
Bridging multiple worlds: multi-marginal optimal transport for causal partial-identification problem
Zijun Gao, Shu Ge, Jian Qian
Under the prevalent potential outcome model in causal inference, each unit is associated with multiple potential outcomes but at most one of which is observed, leading to many caus…