Undergraduate Course: Causal Inference for Social Sciences (PLIT10168)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | The goal of empirical research in social sciences is to reduce the complexity in order to isolate cause and effect. But what exactly is causation and how can it be determined whether an observed relationship is truly causal? This course will provide an overview of the main classes of modelling approaches to causal inference and statistical methods for working with these models using experimental and observational data. |
| Course description |
The world has changed in transformative ways. Data and evidence are everywhere. Quantitative evidence shapes everything from health care to local politics to dating to sports to global security. Critical thinking with data is more important than ever. At the core of the issue is the problem of inferring causality from data. How do we assess whether an observed relationship in data is causal? A common mindset is that causal inference is only possible using randomized experiments, but developments in statistical analysis and related fields have shown that this view is oversimplified and restrictive. We now have a much richer set of tools that enable causal inference from observational data. We will analyze the strengths and weaknesses of these methods, and throughout the course, we will illustrate the methods with applications drawn from various subjects, including elections, crime, terrorism, health care and sports. The goal of this course is to provide students with adequate methodological skills for conducting causal empirical research in their own fields of substantive interest.
The structure of the course will mix lectures, discussions, and computer work. Students will learn these methods through practical applications with data and coding. That being said, this is not a computing course. The practical sessions will be held using R, but STATA codes for the same task will also be provided before the sessions. Students are free to choose which software to use.
Topics may include:
1- Introduction and Potential Outcomes Model
2- Correlation and Causation
3- Randomized Experiments
4- Controlling for Confounders
5- Instrumental Variables
6- Differences-in-differences
7- Regression Discontinuity Designs
8- The Limits of Quantitative Reasoning
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Information for Visiting Students
| Pre-requisites | Visiting students should have at least one course that covers basic statistical analysis including regression at grade B or above (or be predicted to obtain this). Only university/college level courses will be considered. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
1. Fortnightly in class quizzes: 5 times %5 = %25 total
2. Blog post (1500 words) %25
3. Final essay on a research question of students' choice (3000 words) %50 |
| Feedback |
Feedback on all assessed work shall normally be returned within three weeks of submission. Where this is not possible, students shall be given clear expectations regarding the timing and methods of feedback. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Know what assumptions are required to make causal claims with experimental and observational data.
- Make causal inference from experimental data.
- Perform independent study on new developments of causal inference.
- Know what questions to ask others who are making causal arguments using quantitative evidence.
- Design their own research to maximize the credibility of causal claims.
|
Reading List
- Hernan M. A. and Robins, J. M. (2020) Causal Inference: What If. Chapman & Hall.
- Angrist, J. D. and Pischke, J. S. (2008) Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
- Morgan, Stephen L. and Christopher Winship. (2015) Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press.
- de Mesquita, E. B., & Fowler, A. (2021). Thinking clearly with data: A guide to quantitative reasoning and analysis. Princeton University Press. |
Additional Information
| Graduate Attributes and Skills |
- Analytical thinking - analyse facts and situations and apply creative thinking to develop the appropriate solutions.
- Knowledge Integration and Application - use information and knowledge effectively in order to abstract meaning from information and to share knowledge across fields, including the use of quantitative skills.
- Independent Research - conduct research and enquiry into relevant issues through research design, the collection and analysis of quantitative and qualitative data, synthesising and reporting.
- Analytical thinking - analyse facts and situations and apply creative thinking to develop the appropriate solutions.
- Knowledge Integration and Application - use information and knowledge effectively in order to abstract meaning from information and to share knowledge across fields, including the use of quantitative skills.
- Independent Research - conduct research and enquiry into relevant issues through research design, the collection and analysis of quantitative and qualitative data, synthesising and reporting.
- Creativity and inventive thinking - developing higher-order thinking and sound reasoning.
- Decision making - being able to make, implement and review decisions based on appropriate techniques.
- Change management - formulate, evaluate and apply evidence-based solutions and arguments.
- Written communications - be able to communicate complex ideas and arguments in writing using a range of media from formal writing to social media. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Ugur Ozdemir
Tel: (0131 6)50 3990
Email: Ugur.Ozdemir@ed.ac.uk |
Course secretary | Mr Ethan Alexander
Tel: (0131 6)50 4001
Email: Ethan.Alexander@ed.ac.uk |
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