ECM703-Advances in Causal Inference

Module Provider: School of Politics, Economics and International Relations
Number of credits: 20 [10 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Prof Sarah Jewell

Type of module:

Summary module description:

This module introduces research students to advanced microeconometrics techniques, focusing on methods for causal inference. Students will be expected to have a good knowledge of level 7 econometries (MSc level). The module considers how to select and apply modern and widely used microeconometric techniques for applied research. In addition, students will develop their econometric software skills, primarily the module will make use of Stata but some applications will  make use of R. A beginner’s working knowledge of Stata will be assumed, or students will have to attain this on their own in advance. Materials to help learn Stata will be provided in advance.


The aim of this module is to provide students with a knowledge and understanding of microeconometrics for research, which will allow them to engage with the latest applied and theoretical literature. The module will teach students how to apply microeconometric methods for causal inference, using the statistical software Stata, although some applications will make use of R.

Assessable learning outcomes:

By the end of the module students should:

  1. have the knowledge and understanding required to select and use appropriate microeconometric techniques for research;

  2. have a good understanding and knowledge of causal inference;

  3. be able to devise an identification strategy;

  4. be able to perform their own data analysis using an appropriate statistical package;

  5. be able to critically evaluate methods and approaches chosen by research papers.

Additional outcomes:

Knowledge of statistical and econometric software commensurate with beginning PhD-level research.

Outline content:

Topics may include but not be exclusive to: instrumental variables, difference-in-differences, regression discontinuity design, synthetic controls, machine learning.

Brief description of teaching and learning methods:

Teaching will be via a combination of pre-recorded lectures, required readings and exercises in preparation for online live applied sessions.

There will be pre-recorded lectures to be watched in advance of online live applied sessions: sessions will be 2 hours and may include applied demonstrations/exercises using statistical software and discussion of research papers. Introductory and refresher material will be provided and students will have the opportunity to ask questi ons on these.

Contact hours:
  Autumn Spring Summer
Lectures 20
Practicals classes and workshops 22
Guided independent study:      
    Wider reading (independent) 30
    Wider reading (directed) 20
    Preparation for seminars 30
    Carry-out research project 78
Total hours by term 0 200 0
Total hours for module 200

Summative Assessment Methods:
Method Percentage
Written assignment including essay 20
Project output other than dissertation 80

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

There will be a project proposal worth 20% and one project worth 80%. The project will involve devising an identification strategy and applying methods learnt in the module to answer a causal research question of your choice. The proposal will provide a chance to get feedback and advice on the project ideas and identification strategy.

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy “Penalties for late submission for Postgraduate Flexible programmes”, which can be found here:
The Support Centres will apply the following penalties for work submitted late:

  • where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.
The University policy statement on penalties for late submission can be found at:
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

50% overall, though PhD programme training normally requires 60% average over all credits to be deemed a pass before confirmation of registration.

Reassessment arrangements:

PhD students can retake in the following year if their learning needs analysis requires it. MSc students will submit a new project.

Additional Costs (specified where applicable):

1) Required text books:  

2) Specialist equipment or materials:  Access to Stata 18 through University licence

3) Specialist clothing, footwear or headgear:  

4) Printing and binding:  

5) Computers and devices with a particular specification:  

6) Travel, accommodation and subsistence:  

Last updated: 3 August 2023


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