Yikun Zhang (he/him)

Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments
Yikun Zhang, Yen-Chi Chen
Statistical methods for causal inference with continuous treatments mainly focus on estimating the mean potential outcome function, commonly known as the dose…

Nonparametric Inference on Dose-Response Curves Without the Positivity Condition
Yikun Zhang, Yen-Chi Chen, Alexander Giessing
Existing statistical methods in causal inference often assume the positivity condition, where every individual has some chance of receiving any treatment level…

Efficient Inference on High-Dimensional Linear Models with Missing Outcomes
Yikun Zhang, Alexander Giessing, Yen-Chi Chen
This paper is concerned with inference on the regression function of a high-dimensional linear model when outcomes are missing at random. We propose an…

Transfer Learning Through Conditional Quantile Matching
Yikun Zhang, Steven Wilkins-Reeves, Wesley Lee, Aude Hofleitner
We introduce a transfer learning framework for regression that leverages heterogeneous source domains to improve predictive performance in a data-scarce target…

SCONCE: A cosmic web finder for spherical and conic geometries
Yikun Zhang, Rafael S. de Souza, Yen-Chi Chen
The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of…

Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data
Yikun Zhang, Yen-Chi Chen
This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge…

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
Yikun Zhang, Yen-Chi Chen
Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental…

A Practical Introduction to Regression-based Causal Inference in Meteorology (II): Unmeasured confounders
Caren Marzban, Yikun Zhang, Nicholas Bond, Michael Richman
One obstacle to ``elevating" correlation to causation is the phenomenon of confounding, i.e., when a correlation between two variables exists because both…

A Practical Introduction to Regression-based Causal Inference in Meteorology (I): All confounders measured
Caren Marzban, Yikun Zhang, Nicholas Bond, Michael Richman
Whether a variable is the cause of another, or simply associated with it, is often an important scientific question. Causal Inference is the name associated…

Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach
Yikun Zhang, Yen-Chi Chen
The set of local modes and density ridge lines are important summary characteristics of the data-generating distribution. In this work, we focus on estimating…

The EM Perspective of Directional Mean Shift Algorithm
Yikun Zhang, Yen-Chi Chen
The directional mean shift (DMS) algorithm is a nonparametric method for pursuing local modes of densities defined by kernel density estimators on the unit…