8 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…
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 Inference for Counterfactual Regression under Intervention-Driven Shift
Kwangho Kim
We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data. This problem is central to decision-ma…
Hierarchical and Density-based Causal Clustering
Kwangho Kim, Jisu Kim, Larry A. Wasserman +1
Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating heterogeneous treatment effects pose significant chall…
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…
Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes
Kwangho Kim, Bijan A. Niknam, José R. Zubizarreta
Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the wei…