31 citations · 194 across the 34 of their papers we have counts for
9 papers · 1 filter
Insights on Variance Estimation for Blocked and Matched Pairs Designs
Nicole E. Pashley, Luke W. Miratrix
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and co…
Beyond the Sharp Null: Randomization Inference, Bounded Null Hypotheses, and Confidence Intervals for Maximum Effects
Devin Caughey, Allan Dafoe, Luke Miratrix
Fisherian randomization inference is often dismissed as testing an uninteresting and implausible hypothesis: the sharp null of no effects whatsoever. We show that this view is over…
Shape-constrained partial identification of a population mean under unknown probabilities of sample selection
Luke W. Miratrix, Stefan Wager, Jose R. Zubizarreta
A prevailing challenge in the biomedical and social sciences is to estimate a population mean from a sample obtained with unknown selection probabilities. Using a well-known ratio…
Model-free causal inference of binary experimental data
Peng Ding, Luke W. Miratrix
For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood an…
A Nonparametric Bayesian Methodology for Regression Discontinuity Designs
Zach Branson, Maxime Rischard, Luke Bornn +1
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which plac…
Worth Weighting? How to Think About and Use Weights in Survey Experiments
Luke W. Miratrix, Jasjeet S. Sekhon, Alexander G. Theodoridis +1
The popularity of online surveys has increased the prominence of using weights that capture units' probabilities of inclusion for claims of representativeness. Yet, much uncertaint…
Bridging Finite and Super Population Causal Inference
Peng Ding, Xinran Li, Luke W. Miratrix
There are two general views in causal analysis of experimental data: the super population view that the units are an independent sample from some hypothetical infinite populations,…
Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling
Angela Fan, Finale Doshi-Velez, Luke Miratrix
Latent Dirichlet Allocation (LDA) models trained without stopword removal often produce topics with high posterior probabilities on uninformative words, obscuring the underlying co…
Bounding, an accessible method for estimating principal causal effects, examined and explained
Luke Miratrix, Jane Furey, Avi Feller +2
Estimating treatment effects for subgroups defined by post-treatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challengi…