Optimizing matching and analysis combinations for estimating causal effects.

ABSTRACT: Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with

SUBMITTER: Colson KE 

PROVIDER: S-EPMC4793248 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature