van der Schaar Lab
individualized treatment effect inference

Tutorial series: individualized treatment effect inference

On this page, you will find a series of tutorials on individualized treatment effect inference. Each tutorial offers a mini-syllabus in its own right, and is composed of a selection of modules tailored for specific purposes, time commitments, and levels of familiarity with AI and machine learning.

We will continue to adapt this page over time, adding new videos to introduce and highlight the latest advancements and research. Please continue to check here for new content!

Why is individualized treatment effect inference important?

A major challenge in the domain of healthcare is ascertaining whether a given treatment influences or determines an outcome—for instance, whether there is a survival benefit to prescribing a certain medication, such as the ability of a statin to lower the risk of cardiovascular disease.

Machine learning is capable of enabling truly personalized healthcare; this is what our lab calls “bespoke medicine.”

Bespoke medicine entails far more than providing predictions for individual patients: we also need to understand the effect of specific treatments on specific patients at specific times. This is what we call individualized treatment effect inference. It is a substantially more complex undertaking than prediction, and every bit as important.

Our lab has built a position of leadership in this area. We have defined the research agenda by outlining and addressing key complexities and challenges, and by laying the theoretical groundwork for model development. In our development of algorithms, we have identified and targeted an extensive range of potential clinical applications using both clinical trials and observational data as inputs.

If you’d like to learn more before starting our tutorial series, the link below will take you to a page providing an introduction to individualized treatment effect inference, as well as an overview of some key projects that have driven the entire research area forward.

How to use this tutorial series

This series is comprised of 6 tutorials on individualized treatment effect inference, each of which takes a different approach to the topic.

It’s possible that you’ll want to view the entire series, but unlikely that you’ll need to do so, since the applicability of each tutorial will depend on your familiarity with/intended usage of machine learning and AI, as well as the amount of time you have available.

An overview of the tutorials available can be found below.

A. Fundamentals

A primer offering a basic overview of some of the core concepts of individualized treatment effects. Touches on applications using time-series data.

Course length: short // Level of familiarity: minimal // Intended usage: familiarization

Watch this tutorial

B. Theory

A high-level introduction to the theoretical concepts behind individualized treatment effect inferences. Includes some approaches leveraging automated machine learning.

Course length: short // Level of familiarity: minimal // Intended usage: familiarization

Watch this tutorial

C. Algorithms

An exploration of specific solutions to the complex challenges of developing algorithms for individualized treatment effect inference. Examples include accounting for hidden confounders, working with time-series data, inference for continuous treatments, and more.

Course length: medium // Level of familiarity: aware of common ML model types // Intended usage: algorithm development

Watch this tutorial

D. Applications

Showcase of potential real-world clinical applications of our ITE inference models. Examples include organ transplantation and dosage effect inference.

Course length: medium // Level of familiarity: minimal // Intended usage: real-world usage of models

Watch this tutorial

E1. Advanced (overview)

Rapid runthrough of specific topics and problems in ITE inference.

Course length: short // Level of familiarity: familiar with a range of ML approaches and models // Intended usage: algorithm development

Watch this tutorial

E2. Advanced (in-depth)

In-depth examination of specific topics and problems in ITE inference.

Course length: medium-long // Level of familiarity: familiar with a range of ML approaches and models // Intended usage: algorithm development

Watch this tutorial

Our publications and engagement sessions

A full list of our papers on causal inference, individualized treatment effect inference, and related topics, can be found here.

We would also encourage you to stay abreast of ongoing developments in this and other areas of machine learning for healthcare by signing up to take part in one of our two streams of online engagement sessions.

If you are a practicing clinician, please sign up for Revolutionizing Healthcare, which is a forum for members of the clinical community to share ideas and discuss topics that will define the future of machine learning in healthcare (no machine learning experience required).

If you are a machine learning student, you can join our Inspiration Exchange engagement sessions, in which we introduce and discuss new ideas and development of new methods, approaches, and techniques in machine learning for healthcare.