Course page

ADVANCES IN CAUSAL INFERENCE

provided by University of Reading

Course Details

(last updated: September 2022)


  • Term in which taught: Spring term module

  • Application deadline: please apply by 19th November to join this course online. In case of class size limits, you should apply as early as possible to avoid disappointment.

  • Pre-requisites: Experience with postgraduate level microeconometrics (e.g., the City University's ShOT Econometrics course taken in the Autumn term before)

  • Specialist equipment or materials: Access to Stata 15, 16 or 17 through a University or personal licence.

  • Summary course description: This course 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.

Further details about this course (click to expand)

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.

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.

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.

Approximate hours of study: weekly 2-hour sessions, including pratical sessions.

When: Monday morning, 10:00-12:00

Summative Assessment Methods:

  • 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: 15%]

  • 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: 85% - submitted in May]

Formative assessment methods: Feedback on group presentations and critical analysis of a recent contribution to causal inference.

Assessment requirements for a pass: 50% overall - though this may be altered by a student's own PhD programme director.

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

How to Apply

In order to apply for this course, you need to (1) send an Application Email to the Instructor and to (2) fill the Confirmation Form at the bottom of this page. Please complete both steps according to the instructions reported below.

Please also indicate:

  1. Your full name

  2. Whether you will undertake the assessment

1. Application Email: instructions

Step 1 of 2: Application Email. Send an email to the Instructor of this course and to the Academic Officer of the host university: names and contact links are reported above in the section 'Course Details' (right-click on the name to copy their email address, or left-click on the name to create a new email with your default software). Please send your application email using your university email account. Sample email:


To: [INSTRUCTOR]

Cc: [ACADEMIC OFFICER]

Object: Application to your online PhD course (ShOT/SEDOT)


Dear Prof [INSTRUCTOR],

My name is [STUDENT NAME], PhD student at [HOME UNIVERSITY]. I would like to attend online your PhD course in [COURSE TITLE], shared through the ShOT/SEDOT network. I would like to [BE ASSESSED/AUDIT the module]. Please let me know if I can join the course, I look forward to hearing from you.

2. Confirmation Form: instructions

Step 2 of 2: Confirmation Form. Please answer all the questions in the Confirmation Form below and press the Submit button in the last page. All fields are required. You will receive an automated notification by email.