Identifying Causal Effects in Research and Evaluation
Published: February 28, 2019
This workshop provided an overview of randomised controlled trials and a range of quasi-experimental approaches that can be adopted to identify what works and doesn't work in social policy interventions.
Tim Maloney
Tim Maloney is Professor of Economics and Co-Director of the Centre for Social Data Analytics (CSDA) at the Auckland University of Technology, and Chief Economist at MSD.
After completing his PhD in Economics at the University of Wisconsin, Tim held academic positions at the University of Missouri and Bowdoin College in the U.S., and the University of Auckland between 1991 and 2010.
The workshop
MSD's Client Experience and Service Design teams hosted a trial design training workshop with Tim, in order to explore formal methods for identifying what works and doesn't work in social policy interventions. Tim shared his knowledge about good practice, highlighting the challenges in achieving robust impact measurement / evidence.
Identifying Causal Effects in Research and Evaluation provided an overview of randomised controlled trials and a range of quasi-experimental approaches that can be adopted to estimate programme effects. Practical examples and actual case studies were used to motivate the potential and drawbacks of each of these approaches.
Presentation slides
-
Topic 1: The experimental ideal
'Identifying causal effects in research and evaluation' presentation, 2019.
Pdf, 1.2 MB
-
Topic 2: Matching approaches
'Identifying causal effects in research and evaluation' presentation, 2019.
Pdf, 1.2 MB
-
Topic 3: Other quasi-experimental methods
'Identifying causal effects in research and evaluation' presentation, 2019.
Pdf, 1.2 MB