Selection on Observables
The USAID-funded MEASURE Evaluation project is hosting a series of webinar discussions of the popular MEASURE Evaluation manual, How Do We Know If a Program Made a Difference? A Guide to Statistical Methods for Program Impact Evaluation (Lance P, Guilkey D, Hattori A, Angeles G, 2014). Each webinar in the series reviews key topics from a chapter through verbal discussion and graphical presentation. The webinar series will enable participants to understand the resources offered in the manual—a learning tool for use of methods to estimate program impact. The series also provides stand-alone training tools for the topics covered. MEASURE Evaluation’s goal with these webinars is to provide a highly interactive learning opportunity to participants (please ask questions!).
The third webinar in the series, “Selection on Observables,” will take place September 13 at 10am EDT and again on September 15 at the same time. Please join us for both one-hour sessions in this two-part webinar. This webinar will consider impact evaluation estimation methods based on an identification strategy that assumes we can observe all factors that influence both program participation and the outcome of interest. This two-part webinar will, roughly, examine the two most popular selection on observables estimation strategies: regression, and matching and related methods (e.g, propensity score-based weighting methods).
The first webinar in the series, held March 31, was entitled “Fundamentals of Program Impact Evaluation.” It addressed the basic challenges of program impact estimation. It can now be viewed online. The second webinar, held on June 29 and entitled “Randomization and Its Discontents,” considered program impact estimation based on randomization of program participation status (typically though a randomized controlled trial). It too can now be viewed online. Subsequent webinars will cover the major remaining quasi-experimental impact estimation techniques: within estimation (such as difference-in-differences) and instrumental variables methods.