6 papers
The V-fold jackknife for semiparametric inference: variance estimation, confidence intervals, and simultaneous confidence bands
Yi Li, Ashkan Ertefaie, Mark van der Laan
For decades, the bootstrap has been a default tool for statistical inference because of its broad applicability and minimal analytic requirements. Although its validity is well und…
Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models
Sattwik Ghosal, Xuran Meng, Yi Li
There remain theoretical gaps in deep neural network estimators for the nonparametric Cox proportional hazards model. In particular, it is unclear how gradient-based optimization e…
HAL-MLE Log-Splines Density Estimation (Part I: Univariate)
Yilong Hou, Zhengpu Zhao, Yi Li +1
We study nonparametric maximum likelihood estimation of probability densities under a total variation (TV) type penalty, sectional variation norm (also named as Hardy-Krause variat…
Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks
Yi Li, David Mccoy, Nolan Gunter +3
Modern deep neural networks are powerful predictive tools yet often lack valid inference for causal parameters, such as treatment effects or entire survival curves. While framework…
Regularized Targeted Maximum Likelihood Estimation in Highly Adaptive Lasso Implied Working Models
Yi Li, Sky Qiu, Zeyi Wang +1
We address the challenge of performing Targeted Maximum Likelihood Estimation (TMLE) after an initial Highly Adaptive Lasso (HAL) fit. Existing approaches that utilize the data-ada…
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer
Toru Shirakawa, Yi Li, Yulun Wu +5
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies i…