3 papers
stat.ME2026
Evaluating Algorithm-Assisted Human Decision-Making Over Repeated Algorithm Exposure: Recommendations for Effect Estimands and Experimental Design
Maggie Wang, Michael Baiocchi
In algorithm-assisted decision-making in high-stakes settings like healthcare, an algorithmic decision support tool provides a recommendation, but the human ultimately makes the de…
cs.LG2026
A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
Kara Liu, Maggie Wang, Russ B. Altman
Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When models trained on biased data are d…
stat.ME2024
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…