2 papers
stat.ME2026
On the Equivalence between Neyman Orthogonality and Pathwise Differentiability
Yuxi Chen, Edward H. Kennedy, Sivaraman Balakrishnan
It has been frequently observed that Neyman orthogonality, the central device underlying double/debiased machine learning (Chernozhukov et al., 2018), and pathwise differentiabilit…
math.ST2026
Distribution-uniform anytime-valid sequential inference and the Robbins-Siegmund distributions
Ian Waudby-Smith, Edward H. Kennedy, Aaditya Ramdas
This paper develops a theory of distribution- and time-uniform asymptotics, culminating in the first large-sample anytime-valid inference procedures that are shown to be uniformly…