07-11, 16:05–16:35 (Europe/Amsterdam), Else (1.3)
A lot of industry-available Machine Learning solutions for causal forecasting have a very particular blind spot: unobserved confounders. We will present an approach that allows you to combine state-of-the-art Machine Learning approaches with advanced Econometrics techniques to get the better of both worlds: accurate causal inference and good forecasting accuracy.
Causal Forecasting is a very hot topic in the industry with many applications ranging from marketing spending to pricing. Disentangling causal effects from spurious correlations plays a key role when forecasts are used for decision making, such as in the case of pricing. Solutions available in the industry typically rely on Machine Learning methods that use techniques like DoubleML, Transformers, LSTM, and boosted tree algorithms. A common shortcoming of such solutions is that they do not account for the existence of unobserved confounders, such as world events, or other hard-to-measure effects that can bias the measurement of causal effects. We showcase a solution that was developed over the last 3 years that addresses these challenges by combining advanced Econometrics methods with ML techniques. The case-study will focus on the example of retail pricing, but the solution is broadly applicable and it has been tested in different settings, including airline pricing.
The intended audience:
Targeted at data scientists, product managers, and anyone interested in data-driven decision-making. Ideal for those curious about forecasting and causal inference in sectors like pricing.
Background required:
Open to all levels, no prior knowledge needed. Concepts will be explained simply, focusing on practical insights without complex math.
The takeaway for the audience:
The audience will learn about some common pitfalls of available forecasting solutions and how to resolve them by separating causal relationships into a separate model.
Marc Nientker transitioned from a successful seven-year academic career in econometrics, where he contributed as a PhD and Assistant Professor, to the business world to apply his knowledge on a broader scale. He co-founded Acmetric, a strategic data science consultancy that focuses on transforming businesses through data-driven insights.
Acmetric specializes in practical applications of econometrics in areas such as pricing, inventory optimization, product allocation, measurement, and more. His expertise supports organizations in understanding and implementing data-centric strategies that naturally lead to more informed decision-making and operational efficiencies.