The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data…

We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…

Data sets from large-scale longitudinal surveys involving young children and families have become available for secondary analysis by researchers in a variety of fields. Researchers in early intervention have conducted secondary analyses of such data sets to explore relationships between nonmalleable and malleable factors and child outcomes, and…

In this study, smoothing and scaling approaches are compared for estimating subscore-to-composite scaling results involving composites computed as rounded and weighted combinations of subscores. The considered smoothing and scaling approaches included those based on raw data, on smoothing the bivariate distribution of the subscores, on smoothing…

The purpose of this study is to study the performance of different methods (inverse probability weighting and estimation of informative bounds) to control for differential attrition by comparing the results of different methods using two datasets: an original dataset from Portland Public Schools (PPS) subject to high rates of differential…

The Schools and Staffing Survey (SASS) is conducted by the National Center for Education Statistics (NCES) on behalf of the United States Department of Education in order to collect extensive data on American public and private elementary and secondary schools. SASS provides data on the characteristics and qualifications of teachers and…