Introduction to Double Robust Estimation for Causal Inference
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Updated
Oct 2, 2018
Introduction to Double Robust Estimation for Causal Inference
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231
SuperLearner R package: prediction model ensembling method
Hack Aotearoa 2020
Workshop (2-6 hours): cleaning, missing value imputation, EDA, ensemble learning, calibration, variable importance ranking, accumulated local effect plots. WIP.
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Super LeArner Predictions using NAb Panels
Implementing Gradient Boosting & SuperLearner in R and compare the classification accuracy of the two methods.
Ensemble feature ranking for SuperLearner variable selection
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)
A parallel implementation of the Super Learner estimator in Python. Winner of the Statistical Learning course contest!
A collection of additional screening algorithms for SuperLearner
Ensembled Feature Selection using Cross-Validated SuperLearner
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
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