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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-Day Seminar Taught by Rocío Titiunik, Ph.D.

This two-day seminar focuses on methods for the analysis and interpretation of Regression Discontinuity (RD) designs. It will cover both introductory concepts and recent methodological developments.

The RD design is a non-experimental method that has high internal validity for estimating treatment effects. The design can be used when individuals are assigned to some treatment based entirely on a score—in Education, this score is usually referred to as a “pretest score”. This could be any quantitative measure, such as an exam grade, income, age, or cholesterol level. All individuals whose score exceeds a predetermined cutoff are offered the treatment, while all individuals below the cutoff are not offered the treatment. For example, if a scholarship is given only to students who score 90 or more points in an exam, the effect of the scholarship could be analyzed with a RD design.

After treatment, an outcome is measured for all individuals–the “posttest score” in Education–which could either be the same variable as the pretest score or a different measure. The analysis focuses on detecting possible discontinuities in the observed relationship between the pretest score and the outcome of interest at the cutoff, under appropriate continuity or local randomization assumptions.

RD designs appear naturally in cases where a policy is given to those who are most deserving or in greatest need. For example, a RD design might be implemented by ordering individuals from poorest to richest, and giving financial aid to the poorest individuals first until the budget has been exhausted. Although this would result in the treatment group being much poorer than the control group, the RD design relies on the assumption that near the cutoff, treated and control individuals would have had very similar outcomes in the absence of the treatment. Thus, one appealing feature of the RD design is that, if the required assumptions are met, it allows researchers to make internally valid causal inferences for those at the margin of switching from control to treatment assignment.

The course will discuss different assumptions under which the change in treatment status at the cutoff can be used to study causal treatment effects on outcomes of interest. The focus will be on methodology and empirical practice, not on theoretical results. The statistical and econometric theory underlying the results will be discussed at a conceptual, non-technical level.