Difference-in-differences in depth: A field guide to modern methods (January)

Description

Register now for IFS–Cemmap's intermediate/advanced course on modern difference-in-differences methods.

Course overview

Textbook difference-in-differences (DiD) can conveniently be estimated using a simple least squares regression. Until 10 years ago, this approach was also assumed to work in more complicated settings, e.g. where treatment is non-binary or rolled out over time, or where parallel trends only holds conditional on covariates.

How wrong we were! Over the last decade, many important problems with this approach have been uncovered, affecting almost every departure from the textbook setting. At the same time, new methods have been developed that address these challenges, carefully defining parameters of interest and ensuring methods successfully recover them.

This course will provide detailed, hands-on training of the key developments in this new DiD literature, ensuring participants leave well equipped to tackle the wide range of practical challenges encountered implementing DiD in the wild. It will be particularly suited to civil servants and practitioners working in policy evaluation, those in industry applying causal inference techniques, and PhD students and junior faculty who need a thorough grounding for their research.

Course content

The course will cover the following six topics

Fundamentals A review of the textbook setting including identification and alternative approaches to estimation

Covariates Why throwing covariates into a linear regression doesn’t work, and what to do instead depending on what form the covariates take

Functional form What functional form assumptions DiD makes, why this matters, and implications for how to choose functional form

Treatment A detailed exploration of different treatment variants – both in terms of timing (staggered adoption and non-absorbing), and type (multi-valued discrete and continuous) – the problems they create, and new methods developed for each

Standard errors Why packaged standard errors can be wildly misleading and how to deal with different error structures, including serial correlation and clustering

Assumptions What restrictions parallel trends imposes, challenges with common approaches to validate parallel trends, and more robust alternatives

Course format

The course runs in-person over three consecutive days with around seven hours of lectures, hands-on exercises and Q&A each day.

Breakdown of topics

Day 1

Day 2

Day 3

Indicative class timings

09:30 - 11:00 Lecture and exercises

11:00 - 11:15 Break

11:15 - 12:45 Lecture and exercises

12:45 - 13:45 Lunch

13:45 - 15:15 Lecture and exercises

15:15 - 15:45 Break

15:45 - 17:00 Lecture and exercises

Trainer bio

Jonathan Shaw is an applied economist with over 20 years’ experience using quasi-experimental methods to evaluate labour market policies and study the impact of financial products. He has published in some of the top economics and finance journals as well as providing technical advice for government departments and central banks in the UK and abroad. He currently works at the Financial Conduct Authority and is a Research Associate at the Institute for Fiscal Studies. His website is hereand CV is attached here.

Course prerequisites

This is a graduate-level course targeted towards those with a solid grasp of the textbook DiD model e.g. at the level of the IFS-Cemmap Policy Evaluation Methods training course or the DiD chapter in Mostly Harmless Econometrics (Angrist and Pischke, 2008). Please ensure you have this as a baseline before the course.

The material assumes a working knowledge of causal inference and coding and includes a mixture of econometric principles, simple proofs and hands-on exercises in Stata or R. More complicated maths will be in appendices rather than covered directly in lectures, but you do need to know potential outcomes notation and how to work with conditional expectations, as well as concepts like identification, estimation, consistency and efficiency. This document gives an indication of the level of maths involved.

Participants must bring their own laptop with either Stata or R installed, along with all relevant packages (a list of which will be provided in advance).

Learning outcomes

After completing the training, participants will

Preparatory reading

Please ensure you are happy with the level of maths described in this document.

Please also ensure you have a grasp of the textbook DiD model at the level of the DiD chapter in Mostly Harmless Econometrics (Angrist and Pischke, 2008), or the “DiD Fundamentals” chapter in Causal Inference: The Remix (Cunningham, 2026, available free online here)

Beyond that, no preparatory reading is required for this course.

The following two resources give a reasonable sense of the type of material to be covered, though the course will be at a somewhat more technical level. There is no requirement to read this material beforehand.

Cunningham, S. (2026). Causal inference: The Remix. Yale University Press. [“Complex DiD Designs” chapter; available free online here]

Roth, J., Sant’Anna, P. H., Bilinski, A., & Poe, J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218-2244.

Past participant feedback

Probably the best formal training course I have done for professional development

I really appreciated the depth and completeness of overview of contemporary issues and solutions in the DiD space. It was very much worth the financial and time investment.

Outstanding content with thorough analysis behind it. Exceeded all expectations.