Design-Based Inference

Prof. Peter Hull

Starting April 22nd, 2024

This three-day workshop covers a wide range of practical results for regression and IV-based analyses of causal effects which leverage random or conditionally as-good-as-random shocks. Questions of particular focus include:
  • "What controls do I need to include to avoid omitted variables bias?"
  • "Do I need to worry about 'negative weighting' of heterogeneous effects?"
  • "How should I be clustering my standard errors?"
  • "What's the payoff to considering nonlinear/'structural' analyses?"
Results will be illustrated through several real-world applications.

This is one of our advanced courses. These courses are designed assuming a solid foundation in the basics of causal inference and will cover advanced methods. A solid understanding of the material covered in the material from Scott's courses (Part 1 and Part 2) will be assumed.

All course material is available free and open source via our Github Repository .

Register Today

Sign-up today to ensure access to this workshop.


  • Attendance to the workshop with Prof. Peter Hull.
  • Complete set of example code to implement the methods discussed.
  • PDF lecture slides and video recordings for later reference.

Our workshop will bring you to the cutting edge

April 22nd

Day 1 6pm-9pm EST

  1. Lecture 1: Introduction; Regression Recap
  2. Lecture 2: Selection on Observables
  3. Take-home Application

April 24th

Day 2 6pm-9pm EST

  1. Lecture 3: Negative Weights
  2. Lecture 4: Clustering
  3. Take-home Application

April 26th

Day 3 6pm-9pm EST

  1. Lecture 5: Recentered IV
  2. Lecture 6: Nonlinear Models
  3. Take-home Application

Who will be hosting this session?

Prof. Peter Hull
Peter Hull is the Groos Family Assistant Professor of Economics at Brown Univeristy and a Faculty Research Fellow at the National Bureau of Economic Research. He has published papers on topics in applied econometrics, education, healthcare, and criminal justice, in outlets such as the American Economic Review, the Quarterly Journal of Economics, the Review of Economic Studies, and the New England Journal of Medicine. His research is focused on developing and applying new instrumental variable methods to measure the quality of institutions, such as schools or hospitals, as well as discrimination and bias in human and algorithmic decision-making. Prior to Brown, Professor Hull taught at the Kenneth C. Griffin Department of Economics at the University of Chicago and worked at Microsoft Research and the Federal Reserve Bank of New York. He earned his PhD in economics from MIT in 2017, under 2021 Nobel Laureate Josh Angrist.
  • Using your Saturday to get a world-class tutorial in instrumental variables that would cost many thousands of dollars (or hours of RA work) to get otherwise is a pretty good deal. Peter is super clear and clearly prepared really well for the workshop, with great materials and interesting simulations and applications.

  • The course is very organized and gives a comprehensive overview of IV designs and its recent advances. Peter is very kind and seems to be truly interested in making students understand the lessons.

  • This workshop was expertly run. The exposure to a really amazing instructor in Peter Hull was valuable in and of itself. The lectures were well prepared, lots of helpful readings and lecture notes were provided.

Frequently Asked Questions

Are discounts available?

Yes! Students, postdocs, predocs and residents of middle-income countries can attend for $50 plus a few dollars in fees. Non-tenure track faculty can attend for $95. To receive your promo code, please include a photo of your student ID. International folks from low-income countries can attend for $1. To receive promo codes, email us at

How do I access the material I need for the course?

The course material will be availabe forever on Github. We will also send you links to the video recordings on Vimeo after the workshop is completed.

How long will it take me to master this?

That's a great question. Causal inference, and econometrics more generally, is largely a “returns to experience” type of skill as much as it is a returns to education. The best way for you to learn anything in these classes is to work on projects that require it. Our class is designed as a fast track to both.

Will we practice programming?

Yes, I will distribute assignments with readings with directions the night before. We will help each other in Discord, asking questions, pointing out mistakes I'm making, and helping one another problem solve. I will usually assign more than we can do that faster workers always have something to work on. And in the end, I will distribute the solutions. It'll be fun I promise!

Will there be recordings?

We will upload recordings to Vimeo and they will be password protected, so that only attendees can watch the videos.

I'm nervous that I can't handle the difficulty of the class.

Don't be. I'm a good teacher. If I can learn this, so can you.