Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream Seminar Taught by Ashley Naimi, Ph.D

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.

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.