6 accepted papers
In modern machine learning pipelines, abundant pretrained representations act as noisy proxy covariates while task-specific labels remain scarce. We study semi-supervised regression in this noisy-covariate setting and propose a simple two-stage estimator. We derive finite-sample generalization bound…
Estimating causal effects becomes particularly challenging when outcomes possess complex, non-Euclidean structures, where conventional approaches often fail to capture meaningful structural variation. We introduce a novel framework for topological causal inference, defining treatment effects through…
Understanding treatment effect heterogeneity is vital for scientific and policy research. However, identifying and evaluating heterogeneous treatment effects pose significant challenges due to the typically unknown subgroup structure. Recently, a novel approach, causal k-means clustering, has emerge…
We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-of…
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a general counterfactual classifier, where we can incorporate flexible constraints by casting the classification problem as…
We propose PLLay, a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure. In this work, we show differentiability with respect to layer inputs, for a general persi…