4 papers · 1 filter
Data-Poisoning Audits for Causal Effect Estimation
Kwangho Kim
Observational causal analyses increasingly pool records across sites, vendors, and collection systems, creating vulnerability to append-only attacks in which plausible records are…
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
Kwangho Kim, Jisu Kim
In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce. We study semi-supervised r…
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
Kwangho Kim
We study counterfactual distribution learning for high-dimensional outcomes whose laws may concentrate near lower-dimensional structure. Standard isotropic smoothing ignores this g…
Topological Causal Effects
Kwangho Kim, Hajin Lee
Estimating causal effects is particularly challenging when outcomes arise in complex, non-Euclidean spaces, where conventional methods often fail to capture meaningful structural v…