Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of social and behavioral science disciplines.
Propensity Score Optimized Matching
This chapter focuses on optimized matching. It presents optimizing criteria, explains procedures for achieving optimized matches, and assesses the adequacy and sufficiency of the optimized match. It clarifies differences in the procedures for maximizing the number of cases with those for minimizing the distances between groups. It compares optimized matching with the more general approach of genetic matching and discusses the trade-offs between them.
Researchers have referred to matching procedures that minimize the total distance or dissimilarity between matched cases as optimal matching as well as optimized matching (Rosenbaum, 1989; Rubin, 1979). An optimized matching solution reduces average pregroup imbalance to a minimum, though it may leave some individual confounders slightly imbalanced. If total baseline imbalance cannot be reduced to zero ...