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Found 26 papers, 8 papers with code
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning.
Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curve
Third, we provide consistency and convergence rate for the optimizer of the regimen-response curve estimator; this enables us to estimate an optimal semiparametric rule.
Multi-task Highly Adaptive Lasso
We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL).
Adaptive Sequential Surveillance with Network and Temporal Dependence
Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint.
Why Machine Learning Cannot Ignore Maximum Likelihood Estimation
The growth of machine learning as a field has been accelerating with increasing interest and publications across fields, including statistics, but predominantly in computer science.
Personalized Online Machine Learning
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization.
Adaptive Sequential Design for a Single Time-Series
The current work is motivated by the need for robust statistical methods for precision medicine; as such, we address the need for statistical methods that provide actionable inference for a single unit at any point in time.
Targeting Learning: Robust Statistics for Reproducible Research
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence.
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms
To the best of our knowledge, our proposal is the first one to be rate-adaptive for a collection of general black-box contextual bandit algorithms: it achieves the same regret rate as the best candidate.
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at $n^{-1/3}$-rate to the true weighting mechanism.
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials
We propose nonparametric methodology for efficiently estimating a counterfactual parameter that quantifies the impact of a given immune response marker on the subsequent probability of infection.
Methodology
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits
We propose the Generalized Policy Elimination (GPE) algorithm, an oracle-efficient contextual bandit (CB) algorithm inspired by the Policy Elimination algorithm of \cite{dudik2011}.
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated by a different policy, or policies.
Statistical Analysis Plan for SEARCH Phase I: Health Outcomes among Adults
This document provides the analytic plan for evaluating adult HIV incidence, health, and implementation outcomes for the first phase of the SEARCH Study.
Applications
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both.
Collaborative targeted inference from continuously indexed nuisance parameter estimators
Say that the above product is not fast enough and the algorithm for the $G$-component is fine-tuned by a real-valued $h$.
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational biology and allied sciences.
Methodology
On Adaptive Propensity Score Truncation in Causal Inference
Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator.
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Collaborative minimum loss-based estimation (C-TMLE) is a novel methodology for causal inference that takes into account information on the causal parameter of interest when selecting a PS model.
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model.
Methodology
Targeted Learning with Daily EHR Data
Electronic health records (EHR) data provide a cost and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings.
Causal inference for social network data
We describe semiparametric estimation and inference for causal effects using observational data from a single social network.
Methodology Statistics Theory Statistics Theory
Sequential Double Robustness in Right-Censored Longitudinal Models
Consider estimating the G-formula for the counterfactual mean outcome under a given treatment regime in a longitudinal study.
Methodology
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms.
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
In this study, we applied and evaluated the performance of the SL in its ability to predict treatment assignment using three electronic healthcare databases.
An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions
We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure.
