Date(s) - 12/09/2016 - 12/10/2016
This Statistical Horizons class, Treatment Effects Analysis, taught by Stephen Vaisey, Ph.D., runs from December 9-10 in Philadelphia, Pennsylvania.
This course offers an in-depth survey of a family of techniques known as treatment-effects estimators. Treatment-effects analysis is a quasi-experimental technique for estimating causal effects from observational data using the potential outcomes or counterfactual framework. These techniques — which include propensity-score matching, inverse probability weighting, and “doubly-robust” estimators — are now widely used in the social sciences, health sciences, and public policy.
The goal of treatment-effects analysis is to identify the causal effect of a treatment on an outcome, such as the effect of a college education on earnings, the effect of divorce on child outcomes, or the effect of a training program on employee productivity. A major advantage of treatment-effects techniques over standard regression methods is that they can produce different estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, an important distinction for policy work.
This seminar will take participants from simple exact matching to recent developments like coarsened exact matching and doubly-robust estimators. Participants will get extensive practical experience by working through case studies from economics, sociology, medicine, and public health.
The fee of $995 includes all course materials. The early registration rate of $895 is available until November 9