2 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
Inspection-Guided Randomization: A Flexible and Transparent Restricted Randomization Framework for Better Experimental Design
Maggie Wang, René F. Kizilcec, Michael Baiocchi
Randomized experiments are considered the gold standard for estimating causal effects. However, out of the set of possible randomized assignments, some may be likely to produce poo…
Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics
Evan T. R. Rosenman, Rina Friedberg, Mike Baiocchi
We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported, and will involve sensitive topics. Our interest is in misreport…
Assignment-Control Plots: A Visual Companion for Causal Inference Study Design
Rachael C. Aikens, Michael Baiocchi
An important step for any causal inference study design is understanding the distribution of the treated and control subjects in terms of measured baseline covariates. However, not…
Propensity Score Methods for Merging Observational and Experimental Datasets
Evan Rosenman, Art B. Owen, Michael Baiocchi +1
This project considers how one might augment a limited amount of data from randomized controlled trial (RCT) with more plentiful data from an observational database (ODB), in order…