Double machine learning for intervention effects

A method that performs well on average may still fail at the tail. This note examines double machine learning for intervention effects with attention to stress cases, feasibility and operational control.

Practitioner lens

A model for double machine learning for intervention effects should state its objective and feasible set explicitly. Hidden penalty weights or relaxed constraints can make an impressive result operationally impossible.

First experiment

Write down the objective, constraints, uncertainty set and decision horizon before selecting a solver or model.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.