Handwritten digit recognition with MNIST & Keras
Targeted Maximum Likelihood Estimation for Hierarchical Data
Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"
A super learner was built by stacking logistic regression, random forest, and gradient boosting models (XGBoost) to predict whether a patient in the cardiac wards needs to be transferred to ICU.
TMLE and one-step estimators built on Riesz representers, unifying efficient estimation across causal inference settings: treatment effects, longitudinal regimes, mediation, quantiles, and two-phase sampling, with super learner nuisance estimation
Sklearn based Super Learning Stacked model
Analysis code for 'Environmental Chemicals as Modifiers of the Association between Chronological Age and Ovarian Reserve' (EARTH Study): a DR-learner analysis of whether 16 endocrine-disrupting chemicals modify the age–antral follicle count association
Nonparametric estimators of mediation effects with multiple mediators
Classify handwritten digits with TensorFlow/Keras, comparing single-layer, ANN, and CNN models on the MNIST dataset to find the best approach.
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