ECM703-Advances in Causal Inference

Module Provider: School of Politics, Economics and International Relations
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites: Only registered PhD candidates in Economics or related disciplines
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Sarah Jewell
Email: s.l.jewell@reading.ac.uk

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 may 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.

Aims:

The aim of this module is to provide students with a knowledge and understanding of microeconometrics, 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 may 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: difference-in-differences and panel data, regression discontinuity design, matching, synthetic controls, instrumental variables, quantile regression, 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.

Ìý

Each week 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.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		20
Practicals classes and workshops		20
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		30
Ìý Ìý Wider reading (directed)		20
Ìý Ìý Preparation for tutorials		20
Ìý Ìý Preparation for presentations		5
Ìý Ìý Preparation for seminars		10
Ìý Ìý Carry-out research project		75
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	200	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Written assignment including essay	15
Project output other than dissertation	85

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

There will be one group presentation on a research paper worth 15% and one project worth 85%. The project will involve devising an identification strategy and applying methods learnt in the module to answer a causal research question of your choice.

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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
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:

None – PhD students can retake in the following year if their learning needs analysis requires it.

Additional Costs (specified where applicable):

1) Required text books:ÌýÌý

2) Specialist equipment or materials:Ìý Access to Stata 16 through University licence

3) Specialist clothing, footwear or headgear:ÌýÌý

4) Printing and binding:ÌýÌý

5) Computers and devices with a particular specification:ÌýÌý

6) Travel, accommodation and subsistence:ÌýÌý

Last updated: 17 October 2022