Luke W Miratrix
Biographic Data
| ID | 1725683 |
|---|---|
| NAME | Luke W Miratrix |
| GIVEN NAMES | Luke W |
| FAMILY NAME | Miratrix |
| SIGNATURE | MIRATRIX L W |
| AFFILIATIONS | Harvard University |
| ORCID | 0000-0002-0078-1906 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 80 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2012 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Multilevel Metamodels
Metamodels, or the regression analysis of Monte Carlo simulation results, provide a powerful tool to summarize simulation findings. However, an underutilized approach is the multilevel metamodel (MLMM) that accounts for the dependent data structure that arises from fitting multiple models to the same simulated data set. In this study, we articulate the theoretical rationale for the MLMM and illustrate how it can improve the interpretability of si…
Sex disparities in Covid-19 outcomes in the United States
Exploring treatment impact heterogeneity across sites
Lurking Inferential Monsters? Quantifying Selection Bias in Evaluations of School Programs
This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within‐study comparisons to the literature, all from primary schools in England. Across these 14 studies, we generate 42 estimates of selection bias using a simple approach to observational analysis. A meta‐analysis of these estimates suggests that the distribution of underlying bias is center…
Matching with Text Data
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 renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into (1) the choice of text repr…
Worth Weighting? How to Think About and Use Weights in Survey Experiments
The popularity of online surveys has increased the prominence of using sampling weights to enhance claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in survey experiment analysis: should they be used? If so, which estimators are preferred? We offer practical advice, rooted in the Neyman–Rubin model, for researchers working with survey experimental data. We examine simple, efficient estimato…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Differential effects of three professional development models on teacher knowledge and student achievement in elementary science
To identify links among professional development, teacher knowledge, practice, and student achievement, researchers have called for study designs that allow causal inferences and that examine relationships among features of interventions and multiple outcomes. In a randomized experiment implemented in six states with over 270 elementary teachers and 7,000 students, this project compared three related but systematically varied teacher intervention…
Worth Weighting? How to Think About and Use Weights in Survey Experiments
The popularity of online surveys has increased the prominence of using sampling weights to enhance claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in survey experiment analysis: should they be used? If so, which estimators are preferred? We offer practical advice, rooted in the Neyman–Rubin model, for researchers working with survey experimental data. We examine simple, efficient estimato…
Sex disparities in Covid-19 outcomes in the United States
Matching with Text Data
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 renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into (1) the choice of text repr…
Lurking Inferential Monsters? Quantifying Selection Bias in Evaluations of School Programs
This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within‐study comparisons to the literature, all from primary schools in England. Across these 14 studies, we generate 42 estimates of selection bias using a simple approach to observational analysis. A meta‐analysis of these estimates suggests that the distribution of underlying bias is center…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Differential effects of three professional development models on teacher knowledge and student achievement in elementary science
To identify links among professional development, teacher knowledge, practice, and student achievement, researchers have called for study designs that allow causal inferences and that examine relationships among features of interventions and multiple outcomes. In a randomized experiment implemented in six states with over 270 elementary teachers and 7,000 students, this project compared three related but systematically varied teacher intervention…
Principal Stratification
Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous subgroups, or principal strata, based on post-randomization behaviors under both the observed and the coun…
Worth Weighting? How to Think About and Use Weights in Survey Experiments
The popularity of online surveys has increased the prominence of using sampling weights to enhance claims of representativeness. Yet, much uncertainty remains regarding how these weights should be employed in survey experiment analysis: should they be used? If so, which estimators are preferred? We offer practical advice, rooted in the Neyman–Rubin model, for researchers working with survey experimental data. We examine simple, efficient estimato…
Lurking Inferential Monsters? Quantifying Selection Bias in Evaluations of School Programs
This study examines whether unobserved factors substantially bias education evaluations that rely on the Conditional Independence Assumption. We add 14 new within‐study comparisons to the literature, all from primary schools in England. Across these 14 studies, we generate 42 estimates of selection bias using a simple approach to observational analysis. A meta‐analysis of these estimates suggests that the distribution of underlying bias is center…
Matching with Text Data
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 renders exact matching infeasible, causes propensity scores to produce incomparable matches, and makes assessing match quality difficult. In this paper, we characterize a framework for matching text documents that decomposes existing methods into (1) the choice of text repr…
Exploring treatment impact heterogeneity across sites
Sex disparities in Covid-19 outcomes in the United States
Multilevel Metamodels
Metamodels, or the regression analysis of Monte Carlo simulation results, provide a powerful tool to summarize simulation findings. However, an underutilized approach is the multilevel metamodel (MLMM) that accounts for the dependent data structure that arises from fitting multiple models to the same simulated data set. In this study, we articulate the theoretical rationale for the MLMM and illustrate how it can improve the interpretability of si…
Mathematics (5 works) · Psychology (5 works) · Computer Science (4 works) · Statistics (4 works) · Advanced Causal Inference Techniques (3 works) · Causal inference (3 works) · Context (archaeology (3 works) · Econometrics (3 works) · Geography (3 works) · Medicine (3 works)