Abstract
Join us for “Causal Machine Learning: Understanding the ‘Why’ in Your Data,” where we will demystify the fundamentals of causal machine learning and its pivotal role in data-driven decision-making. This hands-on workshop will equip you with essential skills to uncover causal relationships and apply them to real-world challenges in academia and industry.
We will delve into key topics, including Double Machine Learning, recent advancements such as personalization, and effect heterogeneity. Whether you are tackling medical treatment personalization or optimizing marketing strategies, this workshop provides the tools and insights to enhance your decision-making with causal analysis.
Objectives
– Learn basic knowledge and tools for causal analysis
– Understand key challenges in Causal ML
– Explore practical examples
– Get hands-on experience with the Python library DoubleML and its workflow
– Engage in discussions and identify pain points in causal analysis
Target Group
Data scientists, data analysts and data managers at all levels
Prerequisites
Laptop, interest in causality and data-based decision-making, preliminary Python skills and data science knowledge are advantageous, optionally sketch typical causal problems from your work