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Found 6 papers, 4 papers with code
Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials
Benkeser et al. demonstrate how adjustment for baseline covariates in randomized trials can meaningfully improve precision for a variety of outcome types.
Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures
For most weeks considered, unadjusted analyses suggested strong associations between mobility indices and subsequent growth in case rates.
Two-Stage TMLE to Reduce Bias and Improve Efficiency in Cluster Randomized Trials
First, outcomes are often missing for some individuals within clusters.
A Primer on Causality in Data Science
Many questions in Data Science are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome interest.
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
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
