7 papers
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…
Causal K-Means Clustering
Kwangho Kim, Jisu Kim, Edward H. Kennedy
Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since t…
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…
Semiparametric Counterfactual Regression
Kwangho Kim
We study counterfactual regression, which aims to map input features to outcomes under hypothetical scenarios that differ from those observed in the data. This is particularly usef…