HP4F2E      Half Unit
Quasi-Experimental Evaluation of Healthcare Programs and Policies

This information is for the 2021/22 session.

Teacher responsible

Dr Elisabetta De Cao COW 3.06


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.


The course will be delivered in no less than 22 hours and consists lectures and seminars.

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

  • Cunningham, Scott. Causal Inference: The Mixtape. Yale University Press, 2021. JSTOR, www.jstor.org/stable/j.ctv1c29t27. Accessed 12 July 2021.
  • 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.

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Health Policy

Total students 2020/21: Unavailable

Average class size 2020/21: Unavailable

Controlled access 2020/21: 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