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Synthetic Control Methods for Policy Impact Evaluation

Bruno Castanho Silva

University of Cologne

Bruno Castanho is a postdoctoral researcher at the Cologne Center for Comparative Politics, University of Cologne.

He holds a PhD in political science from Central European University and has experience teaching various topics in research design and methodology, including causal inference, machine learning, and structural equation modelling. 

Bruno has written a textbook on Multilevel Structural Equation Modelling in collaboration with fellow Winter School instructors Constantin Manuel Bosancianu and Levi Littvay.


Course Dates and Times

Friday 14 February 13:00–15:00 and 15:30–18:00

Saturday 15 February 09:00–12:30 and 14:00–17:30

Prerequisite Knowledge

You should be familiar with concepts and practices of traditional multivariate regression analysis.

Examples will be given in R or Stata, so you should also have a working knowledge of one of these two softwares. 

Short Outline

Synthetic control methods appeared as a way of conducting causal inference for single case studies. They allow us to identify causal effects of an intervention or policy change in one unit (city, region, country, etc) on an outcome of interest over time. Given their multiple possible applications, these methods have become very popular in economics, political science, and public policy.

This course starts from the basics of the potential outcomes framework underlying the workings of synthetic controls, and works through the various ways to implement them.

We begin with single-case studies, cover inference with placebo tests in space and time, and move to more advanced implementations where there are multiple treated cases, as well as generalisations of synthetic controls.

Tasks for ECTS Credits

1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

Long Course Outline

Synthetic control methods are used for conducting causal inference with longitudinal data. Their popularity comes from the intuitive interpretation of their results, as well as their ability to estimate causal effects on a single treated unit.

These methods work by creating a synthetic unit (city/region/country/etc) that is as similar as possible to the one that has received some sort of intervention (the so-called 'treated' unit), or where a policy has been adopted, except for the intervention/policy itself. Any difference between the real unit and the synthetic one on the outcome of interest is deemed to have been caused by the policy or intervention.

In recent years, there has been an explosion of expansions to synthetic control methods, allowing not only single-case studies but also small, medium, and large-N analysis and inference.

Day 1
The first session starts with the potential outcomes framework (or Rubin Causal Model), which underlies the synthetic controls framework. We discuss the foundations of how and when the estimates of synthetic controls can be causally identified, and introduce the notation used in the course.

We follow with a conceptual discussion on assumptions, applicability, estimation, and inference. We discuss how the weights are assigned, data requirements for estimation, and how to interpret results.

We end this first day with a practical session on implementing synthetic controls. Examples will be given in R, but Stata code will be provided for those who prefer it.

Day 2
We start with robustness and sensitivity tests, namely placebo tests which are used for inference and testing assumptions. We cover placebo-in-time tests, as well as the construction of placebos for significance testing. We also look at sensitivity tests, such as the jackknife, to assess how robust the results are in relation to the untreated units in the sample.

We then move to applications of synthetic controls to multiple treated units, an emerging area in this topic. This session covers how to build synthetic controls for several units, how to assess the fit of controls, calculate an average treatment effect, and bootstrapping methods for testing the significance of results.

After a lab practice, we go to recent advanced applications of synthetic controls, and look at generalised synthetic control models.

By the end of this course you will be able to identify the proper specifications of synthetic controls for your data, how to implement these models and rigorously test their assumptions to make inferences.

Day Topic Details
1 Session 1 13:00–15:00 Introduction to Potential Outcomes Framework and Synthetic Controls

We conceptually introduce the Rubin Causal Model and synthetic controls for single-case studies; the most basic kind.

Lecture + Lab

  • Potential outcomes framework
  • Introduction to synthetic controls
  • Assumptions, data, estimation, interpretation of results
1 Session 2 15:30–18:00

Lecture + Lab

We look into inference and how to interpret and analyse results from synthetic control models.

2 Session 1 09:00–12:30

Practical implementation of the sensitivity and robustness tests in SCM.

Lecture + Lab

  • Placebo tests for single-case studies
  • Multiple treated units
  • Generalised synthetic controls
2 Session 2 14:00–17:30

Lecture + Lab

Advanced applications: multiple treated units, and generalised synthetic controls.

Day Readings

Imbens, Guido W., and Donald B. Rubin. 2015
Causal Inference for Statistics, Social, and Biomedical Sciences: an Introduction, Chapter 1
Cambridge: Cambridge University Press

Abadie, Alberto, and Javier Gardeazabal. 2003
The Economic Costs of Conflict: A Case Study of the Basque Country
American Economic Review, 93 (1): 113–132

Abadie, Alberto, Alexis Diamond, and Jens Hainmüller. 2015
Comparative Politics and the Synthetic Control Method
American Journal of Political Science 59(2): 495–510

Alberto Abadie, Alexis Diamond, Jens Hainmüller
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program
Journal of the American Statistical Association. 2010, 105(490): 493–505


Kreif, Noemi, Richard Grieve, Dominik Hangartner, Alex James Turner, Silviya Nikolova, and Matt Sutton. 2016
Examination of the Synthetic Control Method for Evaluating Health Policies with Multiple Treated Units
Health Economics 25(12): 1514–1528

Cavallo, Eduardo, Sebastian Galiani, Ilan Noy, and Juan Pantano. 2013
Catastrophic Natural Disasters and Economic Growth
The Review of Economics and Statistics 95(5): 1549–1561

Xu, Yiqing
Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Political Analysis 25, no. 1 (2017): 57–76

Software Requirements

You are encouraged to use only open-source software throughout the course. All examples and tutorials will be provided in R, but Stata scripts will also be available.

Please download the most recent versions of R and RStudio or Stata, and ensure they are working properly on your machine.

Hardware Requirements

Please bring your laptop with the above software installed.

Recommended Courses to Cover Before this One

<p>Introduction to R<br /> Introduction to Stata<br /> Linear Regression<br /> General Linear Models<br /> Causal Inference I</p>

Recommended Courses to Cover After this One

<p>Advanced Topics in Regression<br /> Causal Inference II</p>

Additional Information


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc). Registered participants will be informed in due time.

Note from the Academic Conveners

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.