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Econometrics: Cause and Effect

Module description


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.


Additional Information:

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:Econometrics: Cause and Effect
Module code:BEE3072
Module level:3
Academic year:2023/4
Module lecturers:
  • Professor Climent Quintana-Domeque - Convenor
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 2:


Module aims

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 ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity22Lectures
Scheduled Learning and Teaching Activity10Tutorials
Guided Independent Study118Reading, preparation for classes and assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class discussion/exercisesWeeks 2, 4, 6, 81-6Verbal/ELE

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Mid-Term Exam301 hour1-6Tutorial Discussion
Final Exam702 hours1-6Online

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Midterm exam (30%)Midterm exam (1 hour) (30%)1-6August Examination Period
Final exam (70%)Final exam (2 hours) (70%)1-6August Examination Period

Syllabus plan

-Causality in economics

-Potential outcomes framework

-Regression with experimental data

-Regression with observational data

-Experiments in economics

-Quasi-experiments in economics

Indicative learning resources - Basic reading

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.

Module has an active ELE page?


Indicative learning resources - Web based and electronic resources

ELE – College to provide hyperlink to appropriate pages

Origin date


Last revision date