31 citations · 194 across the 34 of their papers we have counts for
40 papers
More power to you: Using machine learning to augment human coding for more efficient inference in text-based randomized trials
Reagan Mozer, Luke Miratrix
For randomized trials that use text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of intere…
Improving the Estimation of Site-Specific Effects and their Distribution in Multisite Trials
JoonHo Lee, Jonathan Che, Sophia Rabe-Hesketh +2
In multisite trials, researchers are often interested in several inferential goals: estimating treatment effects for each site, ranking these effects, and studying their distributi…
Leveraging text data for causal inference using electronic health records
Reagan Mozer, Aaron R. Kaufman, Leo A. Celi +1
In studies that rely on data from electronic health records (EHRs), unstructured text data such as clinical progress notes offer a rich source of information about patient characte…
Improving instrumental variable estimators with post-stratification
Nicole E. Pashley, Luke Keele, Luke W. Miratrix
Experiments studying get-out-the-vote (GOTV) efforts estimate the causal effect of various mobilization efforts on voter turnout. However, there is often substantial noncompliance…
Benefits and costs of matching prior to a Difference in Differences analysis when parallel trends does not hold
Dae Woong Ham, Luke Miratrix
The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption, which is an assertion that the treatment group, absent treatment, wou…
Designing Experiments Toward Shrinkage Estimation
Evan T. R. Rosenman, Luke Miratrix
We consider how increasingly available observational data can be used to improve the design of randomized controlled trials (RCTs). We seek to design a prospective RCT, with the in…
Power Under Multiplicity Project (PUMP): Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-level Experiments
Kristen Hunter, Luke Miratrix, Kristin Porter
For randomized controlled trials (RCTs) with a single intervention being measured on multiple outcomes, researchers often apply a multiple testing procedure (such as Bonferroni or…
Precise Unbiased Estimation in Randomized Experiments using Auxiliary Observational Data
Johann A. Gagnon-Bartsch, Adam C. Sales, Edward Wu +4
Randomized controlled trials (RCTs) are increasingly prevalent in education research, and are often regarded as a gold standard of causal inference. Two main virtues of randomized…
Is it who you are or where you are? Accounting for compositional differences in cross-site treatment variation
Benjamin Lu, Eli Ben-Michael, Avi Feller +1
Multisite trials, in which treatment is randomized separately in multiple sites, offer a unique opportunity to disentangle treatment effect variation due to "compositional" differe…
Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
Devin Caughey, Allan Dafoe, Xinran Li +1
Randomization inference (RI) is typically interpreted as testing Fisher's "sharp" null hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticized…
Block what you can, except when you shouldn't
Nicole E. Pashley, Luke W. Miratrix
Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the…
Conditional As-If Analyses in Randomized Experiments
Nicole E. Pashley, Guillaume W. Basse, Luke W. Miratrix
The injunction to `analyze the way you randomize' is well-known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the…
Hospital Quality Risk Standardization via Approximate Balancing Weights
Luke Keele, Eli Ben-Michael, Avi Feller +2
Comparing outcomes across hospitals, often to identify underperforming hospitals, is a critical task in health services research. However, naive comparisons of average outcomes, su…
Using Simulation to Analyze Interrupted Time Series Designs
Luke Miratrix
We are sometimes forced to use the Interrupted Time Series (ITS) design as an identification strategy for potential policy change, such as when we only have a single treated unit a…
Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
Peter Z. Schochet, Nicole E. Pashley, Luke W. Miratrix +1
This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing…
Lurking Inferential Monsters? Quantifying bias in non-experimental evaluations of school programs
Ben Weidmann, Luke Miratrix
This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within-study comparisons…
A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?
Maxime Rischard, Zach Branson, Luke Miratrix +1
Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a "forcing" variable on one side of a threshold value receive…
Identifying and Estimating Principal Causal Effects in Multi-site Trials
Lo-Hua Yuan, Avi Feller, Luke W. Miratrix
Randomized trials are often conducted with separate randomizations across multiple sites such as schools, voting districts, or hospitals. These sites can differ in important ways,…
Randomization Tests that Condition on Non-Categorical Covariate Balance
Zach Branson, Luke Miratrix
A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment e…
Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality
Reagan Mozer, Luke Miratrix, Aaron Russell Kaufman +1
Matching for causal inference is a well-studied problem, but standard methods fail when the units to match are text documents: the high-dimensional and rich nature of the data rend…
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…