Using ML to find out the "Why"? A Tutorial in Causal Machine Learning

The tutorial will be organized in three blocks.

1) Introduction and motivation

We will point out why Causality matters in data science. Many problems managers and data scientists are facing are causal. When organizations and companies want to optimize their marketing campaigns, their financial planning, pricing scheme they usually run into causal considerations: How much do my sales decrease if we increase the price by X%? How can I send out email newsletters to those who like them and avoid to annoy other subscribers?

Causal Inference and Causal ML offer powerful tools that help to formalize and model things that are usually discussed only on an intuitive basis: Are the people who opened my newsletters really comparable to those who haven't? Can I just compare the convergence rates of these groups when I want to evaluate the newsletters's effectiveness?

2) Introduction to Causal Machine Learning with DoubleML

Causal Machine Learning offers tools to estimate causal relationships with SOTA ML algorithms. We will offer an introduction to the Double Machine Learning approach (Chernozhukov et al., 2018). This introduction will be aligned with several data examples and code demonstrations using the Python package DoubleML, https://docs.doubleml.org/stable/index.html . DoubleML is an open source package that offers various tools to estimate causal effects, for example for estimation of heterogeneous treatment effects (like in personalized marketing or personalized medicine).

3) Hands-on Session: Data Example

The tutorial featues a data projects that participants can solve on their own. With the hands-on session participants already get started on their own Causality learning journey :) Participants are invited to apply DoubleML to their own data example and play around with the package features. The hands-on session will follow the structure of the DoubleML workflow, which guides analysts through the process of causal inference with DoubleML, https://docs.doubleml.org/stable/workflow/workflow.html.

4) Discussion and Q&A

The tutorial conlcudes with a discussion and Q&A session. We are looking forward to participants' comments and ideas. We appreciate fedback of the Python community on the DoubleML package :)