Workshop estimating causal effects of policy interventions

This webpage contains all the materials for a one-day workshop on causal impact assessment. The materials on this website are CC-BY-4.0 licensed.

Course objectives

How do we assess whether a school policy intervention has had the desired effect on student performance? How do we estimate the impact a natural disaster has had on the inhabitants of affected regions? How can we determine whether a change in the maximum speed on highways has lead to fewer accidents? These types of questions are at the core of many social scientific research problems. While questions with this structure are seemingly simple, their causal effects are notoriously hard to estimate, because often we cannot perform a randomized controlled experiment.

In this course, we will deal with several advanced methods for answering such questions, with a dual focus:

• What are the causal assumptions underlying these methods?
• How can we put these methods in practice?

At the end of this workshop, participants have a firm grasp of the basics and limits of causal impact assessment, as well as the skills to start applying these methods in their own work.

Prerequisites

We assume the following:

• you are comfortable with estimating and interpreting regression models

• you are familiar with the R programming language and you have a recent version installed

• it's a bonus if you are somewhat familiar with the tidyverse suite of packages (readr, dplyr, ggplot, tibble)

• you have installed the following R packages on your computer:

  • tidyverse
  • sandwich
  • lmtest
  • tidysynth
  • rdrobust
  • fpp3
  • mice
  • CausalImpact

  You can use the following code to install these at once:

  install.packages(c("tidyverse", "sandwich", "lmtest", "tidysynth", "rdrobust", "fpp3", "mice", "CausalImpact"))

Workshop schedule & materials

Time	Duration	Activity	Content	link
09:00	45	Lecture	Introduction + pre-post + DiD	intro.pdf
09:45	15	Practical	Setup + data intro	intro.html
10:00	25	Lecture	Pre-post + DiD
10:25	20	Practical	Pre-post + DiD
10:45	15	Break
11:00	30	Lecture	Interrupted time series (+RDD)	its.pdf
11:30	30	Practical	Interrupted time series (+RDD)	its.html
12:00	60	Lunch
13:00	45	Lecture	Synthetic control	synthetic_control.pdf
13:45	45	Practical	Synthetic control	synthetic_control.html
14:30	15	Break
14:45	45	Lecture	(synthetic) CITS and CausalImpact	causal_impact.pdf
15:30	45	Practical	(synthetic) CITS and CausalImpact	causal_impact.html
16:15	15	Break
16:30	30	Discussion	Conclusion + open questions	discussion.pdf
17:00		End

You can download the dataset we have prepared from here: proposition99.rds. Save it in a nicely accessible place, we will be using it in every practical.

Additional links

• Course materials for program evaluation for public service
• Causal inference for the Brave and True online python book
• Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects pdf available here
• Forecasting: principles and practice 3rd ed. online R book
• Application of causal impact analysis in marketing at stitch fix

Contact

This project is developed and maintained by the ODISSEI Social Data Science (SoDa) team.

For questions about this course, you can contact us at [email protected], or you can contact the instructors Erik-Jan ([email protected]) or Oisín ([email protected]) directly.

Course logo created by Nithinan Tatah from Noun Project