Social Impact Lab Research

This report is designed for any audience looking to learn more about how machine learning can add new techniques to behavioral design, causal inference, and experimentation.

This tutorial introduces key concepts in machine learning-based causal inference and is an ongoing project with new chapters uploaded as they are completed. Topics currently covered:

• Introduction to Machine Learning
• Average Treatment Effects
• Heterogeneous Treatment Effects
• Policy Evaluation
• Policy Learning
• Panel Data
• Matrix Completion

This course is a series of videos designed for any audience looking to learn more about how machine learning can be used to measure the effects of interventions, understand the heterogeneous impact of interventions, and design targeted treatment assignment policies.

This guide explains what adaptive experiments are, when they can be beneficial, and their limitations. It also offers insights into the questions to ask when considering running adaptive experiments on technology platforms.