Not available in 2019/20
HP4F2E      Half Unit
Quasi-Experimental Evaluation of Healthcare Programs and Policies

This information is for the 2019/20 session.

Teacher responsible

Dr Elisabetta De Cao OLD.1.11


This course is compulsory on the Executive MSc in Evaluation of Health Care Interventions and Outcomes, in collaboration with NICE. This course is not available as an outside option.

Course content

When randomised controlled trials are not possible to conduct due to feasibility, ethical, or policy reasons, quasi-experimental study designs can be used to evaluate the causal impact of health programs and policies. The objective of this course is to teach students how to design, critically appraise, and conduct quasi-experimental studies evaluating health policies, programmes, and interventions. The main focus of the course will be on regression discontinuity designs, interrupted time-series designs, difference-in-differences designs, instrumental variable designs, and synthetic control approaches. This module will provide an overview of these study designs and outline the advantages and disadvantages of each approach with specific examples from the health care literature. Suitability of routinely available healthcare datasets for quasi-experimental evaluation studies will be discussed with seminal examples. Computer workshops will provide the students with hands-on experience in conducting quasi-experimental evaluations.


12 hours of lectures and 10 hours of computer workshops in the LT.

Given the executive nature of this course, it will be offered as an intensive, accelerated, and compressed module with a one-week duration.

Formative coursework

Students will be expected to produce 1 piece of coursework in the ST.

Student will receive detailed feedback on their project report outlines. Feedback received on the project outline will be helpful when developing the final project report. 

Indicative reading

  • William R.. Shadish, Thomas D. Cook, and Donald Thomas Campbell. Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage learning, 2002.
  • Imbens, Guido W., and Donald B. Rubin. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press, 2015.
  • Bärnighausen, Till, et al. "Quasi-experimental study designs series—paper 1: introduction: two historical lineages." Journal of clinical epidemiology 89 (2017): 4-11.
  • Bernal, James Lopez, Steven Cummins, and Antonio Gasparrini. "Interrupted time series regression for the evaluation of public health interventions: a tutorial." International journal of epidemiology 46.1 (2017): 348-355.
  • O’Keeffe, Aidan G., et al. "Regression discontinuity designs: an approach to the evaluation of treatment efficacy in primary care using observational data." Bmj 349 (2014): g5293.
  • Kreif, Noémi, et al. "Examination of the synthetic control method for evaluating health policies with multiple treated units." Health economics 25.12 (2016): 1514-1528.


Project (100%, 3000 words) post-summer term.

Key facts

Department: Health Policy

Total students 2018/19: Unavailable

Average class size 2018/19: Unavailable

Controlled access 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Application of numeracy skills
  • Specialist skills