2023-04-18 –, Kuppelsaal
Identification of causal relationships through running experiments is not always possible. In this talk, an alternative approach towards it, quasi-experimental frameworks, is discussed. Additionally, I will present how to adjust well-known machine-learning algorithms so they can be used to quantify causal relationships.
What problem is the talk addressing?
Experiments are a gold standard for estimating causal relationships. That being said, they are not always possible. Experiments can be costly, long-lasting, unethical, or illegal. In other cases, the underlying assumptions for identification cannot be met, e.g. it is not possible to split subjects into control and treatment groups randomly or avoid interactions between them.
Why is the problem relevant to the audience?
Understanding the magnitude of treatment effects is a premise for designing optimal strategies by policy makers/stakeholders.
What are the solutions to the problem?
Prediction-driven algorithms might not be best-tailored for accurate identification of causal links. In this talk I will show how to shift the goal post of those algorithms from prediction towards identification of treatment effects. First, I will cover classical quasi-experimental frameworks such as difference-in-differences and regression discontinuity design. Then, I shed some light on how to augment those methods with out-of-the-box machine-learning techniques. To this end, orthogonal machine learning will be discussed.
What are the main takeaways from the talk?
I will reiterate that correlation does not imply causation. The audience will get familiarized with causal-inference methods used when laboratory experiments are not feasible. The participants will learn how to adjust off-the-shelf machine-learning algorithms to identify conditional average treatment effects.
Novice
Expected audience expertise: Python –None
Abstract as a tweet –Have you ever wanted to know the causal effect of an action but A/B testing wasn’t an option? Here’s a brief helicopter tour over quasi-experimental methods that can be used instead!
A data scientist with a background in economics and econometrics. Currently working at OLX Group in Berlin, focusing on designing and deploying solutions for marketing optimization and automation.