Ka Yan Cheng
I am a Ph.D. candidate in economics at Emory University, and I am on the 2026–2027 academic job market. I work in econometric theory, where machine learning meets semiparametric estimation.
I grew up in Hong Kong and studied economics at The Chinese University of Hong Kong before moving to Atlanta. Outside research, I love watching movies, enjoy having a good pint of Guinness, and have recently started reading books with no equations in them.
For hiring committees
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Job market paper: Cross-Validation for Ridge Regularization in Debiased Machine Learning [PDF] []
This paper studies cross-validation in debiased machine learning (DML) for linear functionals of a regression function γ0, where γ0 and a debiasing nuisance α0 are estimated with ridge regularization. To ensure valid inference, DML places a joint rate requirement on the estimation errors for γ0 and α0. The feasibility of attaining the required rate depends on the joint complexity of the two. We study cross-validation for ridge-regularized estimators of γ0 and α0 on a shared, ill-conditioned dictionary of growing size. Based on our learning-rate bounds, we characterize the boundary at which the rate condition is achievable in terms of how fast the dictionary can approximate the nuisances. We then propose theory-driven feasible candidate sets for both the dictionary dimensions and the ridge penalties, and formally justify the use of separate cross-validation in DML based on these sets. In particular, we develop an oracle inequality for the resulting nuisance estimation risk. Our simulation results confirm these findings.
- Documents: [CV] [research statement] [teaching statement]
- References: Zheng Fang (chair), Florian Gunsilius, Esfandiar Maasoumi, Alejandro Sánchez-Becerra, Pedro H. C. Sant’Anna