Author

James M. Robins

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Preprint Sep 2026

Improved Variance Estimation in Homoskedastic Nonparametric Random-Design Regression via a Two-Scale Approach

We study estimation of a constant conditional variance $\sigma^2$ in nonparametric regression with a $d$-dimensional random design. This is an important problem, and similar questions arise in causal inference. The regression function is $\beta_b$-H\"older smooth, the design density is $\beta_g$-H\"older smooth and bou...

Edgar Dobriban, Rajarshi Mukherjee, James M. Robins et al. · 0 citations

#machine learning Preprint Sep 2026

Optimal Value Inference for Reinforcement Learning

We study offline inference for the optimal value in reinforcement learning under finite state and action spaces. Two new nuisances are derived as fixed points of a self-induced Bellman equation, in which we approximate the maximum Bellman operator by its softmax correspondence. We propose a debiased estimator through t...

Nan Lu, Ethan Lee, James M. Robins et al. · 0 citations