This tutorial covers some of the fundamental concepts and ideas of causal AI using the DoWhy library in Python. Causal inference is quite different conceptually from standard machine learning, so most people will start out with limited background knowledge. However, basic knowledge of regression analysis and statistical modeling will be helpful for following along. By Paul Hünermund and Jermain Kaminski
We've recently collaborated with Databricks to create a solution accelerator for causal machine learning in their ecosystem. Learn how to leverage tools like MLFlow to facilitate model management in the context of an end-to-end causal machine learning application, taken from data exploration and causal discovery all the way to individualized policy recommendations.
EconML’s DML estimator uses price variations in existing data, estimates individualized responses to incentives.
EconML’s DRIV estimator uses this experimental nudge to interpret experiments with imperfect compliance