While we have witnessed spectacular advancements in Machine Learning over the past months and years, robustness of results and establishment of causal relations remain lacking. During this talk we will walk you through an example of using causal Machine Learning techniques to estimate causal, heterogeneous treatment effects with Open Source Python tooling.

Learning causal relationships - in contrast to mere correlations - is of great importance for many applications where we'd like to learn how to intervene with the real world: To whom should we administer which medical drug? To whom should we offer a marketing voucher? For which automated processed should we make a human expert intervene? In such situations we'd like to rest assured our decision don't just rely correlations - potentially tainted by common confounders. Rather, we'd like to make causal statements about the heterogeneous effects of administering a treatment.

In terms of making this happen, the field of Causal Inference has been able to incorporate progress from Machine Learning in theory. Yet, in practice, applications remain challenging: tooling is still somewhat immature and little examples to follow exist. Therefore, we would like to walk you through a case study of estimating causal, heterogeneous treatment effects with Open Source Python tooling.

Schedule:
- Conceptual introduction to causal effect estimation [5']
- Conceptual introduction to MetaLearners [5']
- Comparison of Python tooling for Conditional Average Treatment Effect Estimation [5']
- Practical case study based on experiment data [10']
- Q&A [5']

Requirements:
- Fundamental knowledge about Statistical Learning or Machine Learning
- Fundamental knowledge about Python

After this session, a participant can expect to have an idea as to what causal, heterogeneous treatment effect estimation is about and how to approach it in Python.