ECM703: Advances in Causal Inference

Module code: ECM703

Module provider: Economics; School of Philosophy, Politics and Economics

Credits: 20

Level: 7

When you’ll be taught: Semester 2

Module convenor: Professor Sarah Jewell , email: s.l.jewell@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 3 April 2025

Overview

Module aims and purpose

This module introduces students undertaking research 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 and R will be provided in advance.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. The knowledge and understanding required to select and use appropriate microeconometric techniques for research;
2. Devise an identification strategy;
3. Perform their own data analysis using an appropriate statistical package;
4. Critically evaluate methods and approaches chosen by research papers.

Module content

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

Structure

Teaching and learning methods

Teaching will be via a combination of pre-recorded lectures and 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/or discussion of research papers. Introductory and refresher material will be provided. 

Study hours

At least 0 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.