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Thomas Kane

Walter H. Gale Professor of Education and Economics, HGSE


Do charter schools improve student outcomes?  Is high dosage tutoring likely to help students catchup following the pandemic?  Education policy debates often boil down to causal claims regarding the efficacy or inefficacy of a program or policy. In order to participate in those debates or to choose a curriculum or intervention for a school district or non-profit in the future, you must become a discerning consumer of quantitative impact evidence. In the course, we will discuss five common approaches to measuring program impacts: random assignment, regression discontinuity, difference-in-differences (or comparative interrupted time series), covariate adjustment (sometimes referred to by the term, “value-added”) and matching. Students will gain experience reading, critiquing and, in some cases, replicating examples of each type of study. We will discuss the key assumptions underlying each approach and students will learn what to look for when deciding whether those assumptions are met. Although the primary goal is to build students’ skills in reading and critiquing evidence of program impact, we will also discuss recent evidence on important education policy topics, such as charter schools, school vouchers, the impact of college financial aid, etc. The course will focus on quantitative impact evaluations, as opposed to qualitative or process evaluations. Many readings are drawn from the U.S. context, but we will also discuss papers that draw from international evidence.

Instructor permission required. Enrollment is limited. Please see the home page of the Canvas course website (HGSE A164) for details on how to apply via a questionnaire. The questionnaire is due Thursday, January 18, 2024, at 3:00pm EST. All students who submit the questionnaire will be notified if they have been admitted or waitlisted by 9pm EST on Thursday, January 18, 2024. If you are accepted but then decide not to take the class, please email the TFs so we can admit another student. Prerequisite: Successful completion of S-040 (HGSE), API-202 (HKS), or prior equivalent training in multiple regression. Also offered by the Graduate School of Education as A-164. Please note, this is a jointly offered course hosted by another Harvard school and, accordingly, students must adhere to the academic and attendance policies of that school.