Introduction to Impact Evaluation - Online

The online one day course (which will be taught over two mornings) will introduce you to various empirical, quantitative methods that can be used to estimate the impact of a specific policy intervention.

These methods can be referred to as “programme evaluation”, “impact assessment”, “causal estimation” or “impact evaluation”. The course assumes basic statistical concepts (mean, median, correlation, expected value, statistical significance and confidence intervals), and algebra is optional.

It does not teach participants how to implement any of these methods using statistical software.

The course covers:

• The evaluation problem, and how randomized experiments solve the problem
• An intuitive explanation of the advantages and disadvantages of matching, including propensity score matching; quasi-experimental methods such as instrumental variables; and difference-in-differences
• It does not teach participants how to implement any of these methods using statistical software

By the end of the course participants will:

• Be able to think about evaluation in terms of “counterfactuals” and “informative contrasts” (or comparisons)
• Be able to explain intuitively the conditions under which propensity score matching, instrumental variables and difference-in-differences are likely to produce unbiased estimates of the impact of an intervention
• Be able to assess whether an actual or proposed design for an impact evaluation is likely to give reliable results, given the nature of the policy under consideration

This course is aimed at Government researchers and analysts interested in quantitative methods for impact evaluation, Third sector researchers and analysts interested in quantitative methods for impact evaluation and PhD students and junior researchers.

Course Description

Programme

Day 1:

09:45 Welcome and Introductions

10:05 Session 1: The Evaluation Problem

c. 10:45-11:05: Group exercise

11:30 Tea/Coffee Break

11:50 Session 2: Matching and Regression as a Strategy to Estimate Causal Impacts

c. 12:30-13:00: Group exercise

13:20 Introduction to (brief!) individual overnight exercise: Case study using matching [TBC]

13:30 Close

Day 2:

09:45 Discussion from overnight exercise

10:00 Session 3: Quasi-Experimental Methods: The regression Discontinuity Design and     Instrumental Variables

c. 11:25 – 11:55: Group exercise

12:00 Tea/Coffee Break

12:15 Session 4: The Differences-in-Differences approach

c. 13:00 – 13:30: Group exercise

13:20 Final remarks. Q&A. Introduction to optional take-home exercise: Case study using Regression Discontinuity Design

13:45 Evaluations and Close

THIS COURSE IS RUN OVER TWO MORNINGS AND IS THE EQUIVALENT OF ONE TEACHING DAY FOR PAYMENT PURPOSES.