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: 2021/2

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 using Stata – a beginner’s working knowledge of Stata will be assumed, or students will have to attain this on their own in advance (e.g., see topics 1-4 ��as a public example. Other materials will be provided.

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 techniques, using the statistical software Stata.

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 the statistical package Stata;


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.

Global context:

Economics is global. Research students can use these methods to study any question they like.

Brief description of teaching and learning methods:

Teaching will via be a combination of pre-recorded lectures, required readings and weekly exercises before online live applied sessions

Each week there will be pre-recorded lectures to be watched in advance of online live seminars on, 90 mins, followed by a “reading group” on, 60 mins, to discuss a recent research paper.

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

• Group presentation and critical review task: small groups will lead the weekly discussions on a paper applying methods related to the week’s lectures. After the discussion, the group will submit a mock referee-style report on the paper, to be graded. [weight: 30%]


• Collaborative research project: in pairs, find data and demonstrate application and understanding of the methods from the course by writing a “letter” type paper on any applied economics question (e.g., in the style of the peer-reviewed journals Economics Letters, Applied Economics Letters or Finance Research letters, i.e., around 2,000 words). [weight: 70% - submitted in May]

Formative assessment methods:

Feedback on group presentations and critical analysis of a recent contribution to causal inference.

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: