Machine Learning and Causal Inference

Prof. Brigham Frandsen

Starting October 21st, 2024

Machine Learning's wheelhouse is out-of-sample prediction, but these powerful methods can be deployed in service of causal inference. This two-session workshop will introduce the basics of machine learning prediction methods, including lasso and random forests and how they feature in causal inference methods like double machine learning (DML) and post-double selection lasso (PDS lasso). The course covers the conceptual and theoretical basis for the methods and also gets into the nuts and bolts of implementation in python and Stata using real-world data.

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

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WHAT'S INCLUDED

  • Attendance to the workshop with Prof. Brigham Frandsen.
  • Complete set of example code to implement the methods discussed.
  • PDF lecture slides and video recordings for later reference.
$595 USD + Fees
Buy Your Ticket
"$50" for Students • $95 for Non Tenure-track Faculty • $1 for Low-income Countries

Our workshop will bring you to the cutting edge

October 21st

Day 1 • 6pm-9pm EST

  1. Question your questions (are you asking a predictive or a causal question?)
  2. What is causality?
  3. What is prediction and how does it differ from causality?
  4. Review standard tools of causal inference (concepts + python/Stata)
  5. RCT gold standard
  6. Multiple regression
  7. Introduce ML prediction tools (concepts + python/Stata)
  8. Prediction objective
  9. Bias-variance tradeoff
  10. Lasso
  11. Random forest

October 22nd

Day 2 • 6pm-9pm EST

  1. Put ML to work in service of causality (concepts + python/Stata)
  2. Post-double selection lasso (PDS)
  3. Double/de-biased machine learning (DML)

Who will be hosting this session?

Prof. Brigham Frandsen
Brigham Frandsen is an associate professor at Brigham Young University after completing his Ph.D. in Economics at MIT, where his dissertation focused on econometric methodology and labor economics. After his Ph.D., Dr. Frandsen was selected as a Robert Wood Johnson Scholar in Health Policy Research at Harvard University where he spent two years in residence furthering his research in econometrics and labor economics, as well as adding health policy to his research agenda. Dr. Frandsen's methodological research focuses on causal inference on distributional effects. He applies these methodologies to questions about the impact of labor market institutions and interventions on education and earnings outcomes. His health policy research deals with the consequences of fragmentation in the U.S. health care system. In addition to research, Dr. Frandsen enjoys hiking and mountain biking with his wife, Christine, and their four children.

  • “

    Insightful, well-explained and hands-on workshop that taught me a lot. Dr Frandsen was super helpful and explained concepts clearly. It was a great mix of interactive, practical exercises and theoretical explanations!

  • “

    Outstanding selection of content and case studies. Excellent instructor. You will come out of this workshop with a very decent overview of Machine Learning methods and their applications in causal inference.

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 causalinf@mixtape.consulting.

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.