Marc Nientker - Causal Forecasting: How to disentangle causal effects, while controlling for ...

When optimizing business decisions, pure forecasting is not enough - you need to understand causal relationships between variables to make effective decisions

Unobserved confounding variables (factors that affect both decisions and outcomes but aren’t tracked) can lead to biased predictions and suboptimal decisions

In pricing optimization, factors like competitor prices, inventory levels, marketing events, and seasonality all affect sales but are often not fully captured in data

To handle unobserved confounders:

• First estimate causal elasticities/effects
• Remove these effects from historical data
• Create forecasts on the normalized data
• Add back the causal effects for final predictions

Cross-sectional and panel data methods can help infer causal effects even when you don’t observe all combinations of variables

For products with visible competition, discount effectiveness depends heavily on competitor pricing - relative position matters more than absolute price

Large retailers need to consider thousands of products and complex interactions between pricing, inventory, marketing and external events

Pure machine learning approaches that don’t account for causality can lead to biased optimization, even if forecasts appear accurate

Proper causal forecasting requires both:

• Methods to estimate causal effects
• Ways to incorporate those effects into predictive models

The process requires iterating between causal inference and forecasting steps - most existing tools don’t handle this workflow end-to-end