Time: October 10, 2014 at 9am to October 11, 2014 at 4pm
Location: Temple University Center City
Street: 1515 Market Street
Website or Map: http://www.statisticalhorizon…
Event Type: training, seminar
Organized By: Statistical Horizons LLC
Latest Activity: Jul 14, 2014
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.