Date(s) - 05/17/2018 - 05/18/2018
9:00 am - 5:00 pm
Jamaica Bay Inn
New course hosted by Statistical Horizons 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. We will cover a variety of topics including: Exact matching; Propensity score matching and weighting; Other forms of non-parametric matching and weighting; Regression adjustment; and Various forms of doubly-robust estimators.
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.
The instructor will use Stata (and some R) to demonstrate the techniques. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with Stata installed. Stata 14 (any flavor) or above is preferred, though Stata 13 can do at least 95% of what we will cover in this course. Students do NOT need any prior knowledge of Stata to be able to complete the exercises. This course is for any who want to learn to apply this family of techniques to observational data. Participants should have a basic foundation in linear and logistic regression.