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Found 11 papers, 2 papers with code
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer
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 in longitudinal problem settings.
Adaptive debiased machine learning using data-driven model selection techniques
For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions.
Lassoed Tree Boosting
Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets.
Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials
First, outcomes are often missing for some individuals within clusters.
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarantees can fail when we use adaptively collected data, such as the result of running a contextual bandit algorithm.
Post-Contextual-Bandit Inference
The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage.
Estimation of population size based on capture recapture designs and evaluation of the estimation reliability
We cover models assuming a single constraint (identification assumption) on the K-dimensional distribution such that the target quantity is identified and the statistical model is unrestricted.
The Optimal Dynamic Treatment Rule SuperLearner: Considerations, Performance, and Application
Additionally, we highlight practical choices when implementing the algorithm, including choice of candidate algorithms, metalearners to combine the candidates, and risk functions to select the best combination of algorithms.
Applications
Performance and Application of Estimators for the Value of an Optimal Dynamic Treatment Rule
In this paper, we study the performance of estimators that approximate the true value of: 1) an $a$ $priori$ known dynamic treatment rule 2) the true, unknown optimal dynamic treatment rule (ODTR); 3) an estimated ODTR, a so-called "data-adaptive parameter," whose true value depends on the sample.
Methodology Applications
Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
In order to optimize the finite sample coverage of the nonparametric bootstrap confidence intervals, we propose a selection method for this sectional variation norm that is based on running the nonparametric bootstrap for all values of the sectional variation norm larger than the one selected by cross-validation, and subsequently determining a value at which the width of the resulting confidence intervals reaches a plateau.
Statistics Theory Methodology Statistics Theory
Finite Sample Inference for Targeted Learning
It relies on an initial estimator (HAL-MLE) of the nuisance parameters by minimizing the empirical risk over the parameter space under the constraint that sectional variation norm is bounded by a constant, where this constant can be selected with cross-validation.
Statistics Theory Statistics Theory
