Machine Learning for Estimating Causal Effects - Online Course
A 3-Day Livestream Seminar Taught by
Ashley NaimiThursday, September 24 —
Saturday, September 26, 2026
10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar counts toward both the Causal Inference Certification and the Machine Learning Certification. Each program includes four expert-led courses designed to build advanced, practical skills. Contact us to learn how to complete a certification and take advantage of special pricing.
Machine learning is increasingly being used to evaluate cause-effect relations with social, economic, health, and business data. When used properly, these tools have tremendous potential to yield robust effect estimates with minimal assumptions. However, both machine learning and causal inference techniques add considerable complexity to an analysis, making proper use a challenge.
In this seminar, you will learn how to minimize biases that result from improper use of machine learning methods to answer practical questions about cause-effect relations in non-experimental data.
Starting September 24, this seminar will be presented as a 3-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. If you can’t join in real time, recordings will be available within 24 hours and accessible for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about the course content
We will discuss how machine learning can be used to relax modeling assumptions, while avoiding problems with machine learning methods that result from the “curse of dimensionality.”
Through practical data and coding examples, you will learn to use cutting-edge “double-robust” machine learning methods (targeted minimum loss-based estimation, augmented inverse probability weighting) to estimate different treatment effects in real and simulated data.
The course will focus on building intuition, with numerous coding examples to gain practical experience.
We will discuss how machine learning can be used to relax modeling assumptions, while avoiding problems with machine learning methods that result from the “curse of dimensionality.”
Through practical data and coding examples, you will learn to use cutting-edge “double-robust” machine learning methods (targeted minimum loss-based estimation, augmented inverse probability weighting) to estimate different treatment effects in real and simulated data.
The course will focus on building intuition, with numerous coding examples to gain practical experience.
Computing
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This seminar will use R for the empirical examples and exercises. To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R and RStudio installed. RStudio is a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.
Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
You should have a sound working knowledge of applied statistical analysis and interpretation, and the use and interpretation of linear and generalized linear regression modeling. Prior experience with machine learning and the counterfactual approach to causal inference will be helpful, but is not required.
You should have a sound working knowledge of applied statistical analysis and interpretation, and the use and interpretation of linear and generalized linear regression modeling. Prior experience with machine learning and the counterfactual approach to causal inference will be helpful, but is not required.
Seminar outline
Day 1
-
- Machine learning for effect estimation: the curse of dimensionality
- Regularization bias with single robust estimators
- Double robustness mitigates regularization bias (how and why)
- Double robust methods: some intuition
- Augmented inverse probability weighting (AIPW)
- Targeted minimum loss-based estimation (TMLE)
Day 2
-
- Introduction to stacking (aka the Super Learner)
- Simple illustration of the Super Learner
- Manually coding the Super Learner
- (Bonus): Super Learning for a dose-response function
- SuperLearner and sl3 packages
- Tuning parameter grids
- Screening algorithms
Day 3
-
- Practical guidance on estimating effects in example datasets
- TMLE3 + sl3 for the ATE, ATT, and ATU
- AIPW + sl3 for the ATE, ATT, and ATU
- Wrapping up: where to go from here?
- Alternative estimands
- Time-dependent exposure and confounder modeling
- Mediation analysis
- Further reading/learning materials
Day 1
-
- Machine learning for effect estimation: the curse of dimensionality
- Regularization bias with single robust estimators
- Double robustness mitigates regularization bias (how and why)
- Double robust methods: some intuition
- Augmented inverse probability weighting (AIPW)
- Targeted minimum loss-based estimation (TMLE)
- Machine learning for effect estimation: the curse of dimensionality
Day 2
-
- Introduction to stacking (aka the Super Learner)
- Simple illustration of the Super Learner
- Manually coding the Super Learner
- (Bonus): Super Learning for a dose-response function
- SuperLearner and sl3 packages
- Tuning parameter grids
- Screening algorithms
- Introduction to stacking (aka the Super Learner)
Day 3
-
- Practical guidance on estimating effects in example datasets
- TMLE3 + sl3 for the ATE, ATT, and ATU
- AIPW + sl3 for the ATE, ATT, and ATU
- Wrapping up: where to go from here?
- Alternative estimands
- Time-dependent exposure and confounder modeling
- Mediation analysis
- Further reading/learning materials
- Practical guidance on estimating effects in example datasets
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.