Causal Effects in Economics
Establishing whether a relationship is causal or not is at the core of economic analysis and its potential for societal impact.
The module will introduce the notion of causality used in modern applied microeconomic research. It will present a set of tools and methods that help to uncover causal relationships in economics and illustrate their applications with real world examples: from the causal effect of minimum wages on employment rates to the effects of education on earnings.
The applications discussed in the module are based on research carried out around the world.
The main aim of this module is to develop a clear understanding of the notion of causality when thinking of a relationship between any two variables, a skill which is highly sought after in the workplace.
Research in Teaching
Students will study several examples of empirical research.
Full module specification
|Module title:||Causal Effects in Economics|
Introduction to Econometrics BEE1023 or Econometrics (BEEM2031)
|Duration of module:||
Duration (weeks) - term 2: |
The aim of this module is threefold: (1) introduce students to the notion of causality in economic research, (2) present a set of tools and methods that help to uncover causal relationships in economics, and (3) illustrate the application of these techniques with real world examples.
ILO: Module-specific skills
- 1. explain the notion of causality in economic research
- 2. explain the notion of identification strategy and different types of identification strategies
- 3. explain the pros and cons of experimental and non-experimental data
ILO: Discipline-specific skills
- 4. link economic theory with data
- 5. critically assess empirical studies in economics
ILO: Personal and key skills
- 6. demonstrate analytical and critical thinking
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Guided Independent Study||118||Reading, preparation for classes and assessments.|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In class discussion/exercises||Weeks 2, 4, 6, 8, and 10 (2 hours / tutorial)||1-6||Verbal/ELE|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Midterm Exam||30||1 hour||1-6||Tutorial discussion and/or online|
|Final Exam||70||2 hours||1-6||Online|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Midterm exam (30%), Final exam (30%)||Final Exam (100%) 1 hour||1-6||August/September Reassessment Period|
-Causality in economics
-Potential outcomes framework
-Regression with experimental data
-Regression with observational data
-Experiments in economics
-Quasi-experiments in economics
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
Basic probability theory, random variables and probability distributions, mathematics of expectations, multivariate distributions, sampling and sampling distributions, estimation, interval estimation and hypothesis testing, simple linear regression, inference in simple linear regression, multiple regression.
Ashenfelter Orley, Phillip B. Levine, and David J. Zimmerman (2002) "Statistics and Econometrics: Methods and Applications", Wiley.
Angrist, Joshua D., and Jörn-Steffen Pischke (2014) "Mastering ’Metrics: The Path from Cause to Effect", Princeton University Press.
Angrist, Joshua D., and Jörn-Steffen Pischke (2009) "Mostly Harmless Econometrics: An Empiricist's Companion", Princeton University Press.
Last revision date