Experimental and Quasi-Experimental Methods for Evaluating Outcomes

  • Summer schools
  • Department of Methodology
  • Application code SS-ME310
  • Starting 2020
  • Short course: Open
  • Location: Houghton Street, London

This course provides an overview of a variety of modern techniques for studying the ability of changes to produce effectiveness outcomes, whether these are policy changes, or observed natural or human events whose causal effects we wish to measure. 

The methods you will study have immediate practical implications for evaluating policy outcomes and their effectiveness, but have broad application to social science problems across many fields. As approaches for making causal inferences from observational data, these techniques have moved to the centre of social science fields such as economics, political science, development, and health policy, where understanding causes and effects is of paramount importance but also where controlled experiments are seldom practically feasible or ethically possible. As such, knowledge of these techniques is increasingly important for consumers and practitioners alike.

Combining basic statistical theory, principles of smart research design, and hands-on experience with real data, this course will give you a basis for being good consumers and practitioners of modern quantitative social science. Through a variety of applied examples drawn from multiple fields of social science and policy, we will see how these methods are applied to evaluate outcomes. In class exercises, we will also see how to implement such techniques using example data, and lectures are complemented with computing exercises using real-world data and the statistical software R.

 The course begins with a brief introduction to various philosophical approaches to causality. We focus the remainder of the course on the potential outcomes framework, which is increasingly the lingua franca of causal analysis in the quantitative social sciences and related fields. Using this framework, we begin with experimental logic, presenting the randomized experiment as the gold standard for causal inference. Following this, we use the potential outcomes framework to apply experimental logic to a variety of common non-experimental settings, a situation often referred to as a quasi-experiment. Quasi-experimental designs covered include matching, instrumental variables, difference in differences, synthetic control, and regression discontinuity.

 This course is aimed at third-year undergraduates, postgraduates, and interested professionals.

Session: Three
Dates: 3 – 21 August 2020
Lecturer: Dr David Hendry


Programme details

Key facts

Level: 300 level. Read more information on levels in our FAQs

Fees:  Please see Fees and payments

Lectures: 36 hours 

Classes: 18 hours

Assessment*:  One take-home examination and one final examination

Typical credit**: 3-4 credits (US) 7.5 ECTS points (EU)

*Assessment is optional

**You will need to check with your home institution

For more information on exams and credit, read Teaching and assessment


An introductory course in statistics, an introductory course in multivariate regression, and familiarity with hypothesis testing.

Familiarity with linear algebra, logistic regression, and the R statistical software will be helpful but are not required

Programme structure

  • Introduction to potential outcomes inferences
  • Randomized experiments
  • Statistical matching techniques
  • Difference-in-differences designs
  • The synthetic control method
  • Instrumental variables
  • Regression discontinuity designs
  • Causal mediation analysis.

Course outcomes

At the end of the course, students will be able to:

  • Summarise the logic of the potential outcomes framework
  • Describe, synthesize, and differentiate between a variety of common social science designs that use the potential outcomes framework
  • Explain the causal identification assumptions required for each technique covered in the class
  • Demonstrate facility for identifying potential confounding factors in causal explanations of social science phenomena
  • Demonstrate facility with implementing the techniques covered in the course using statistical software on real-world datasets.


LSE’s Department of Methodology is an internationally recognised centre of excellence in research and teaching in the area of social science research methodology. The Department coordinates and provides a focus for methodological activities at the School, in particular in the areas of graduate student (and staff) training and of methodological research.

Through its graduate programmes, and the Department's provision of courses for research students from all parts of the School, the aim is to make the School the pre-eminent centre for methodological training in the social sciences.

On this three week intensive programme, you will engage with and learn from full-time lecturers from the LSE’s methodology faculty.

Reading materials

  • Angrist, Joshua D., and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press.
  • Angrist, Joshua D., and Jörn-Steffen Pischke. 2014. Mastering Metrics: The Path from Cause to Effect. Princeton, NJ: Princeton University Press.
  • Gerber, Alan S., and Donald P. Green. 2012. Field Experiments: Design, Analysis, and Interpretation. New York: W.W. Norton & Co.
  • Rosenbaum, Paul R. 2010. Design of Observational Studies. New York: Springer.

*A more detailed reading list will be supplied prior to the start of the programme

**Course content, faculty and dates may be subject to change without prior notice

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