ACT, WAIT, or EXPERIMENT: A Causal Governance Framework for Retail Price Optimization Under Abstentions

RESUMO

This paper presents a causal decision-making framework for estimating price elasticity in retail channels, a process typically confounded by promotions, competitor movements, and market frictions. Rather than forcing a calculation when data is ambiguous, the system introduces decision abstention (\textsc{wait}) as an active diagnostic tool rather than an estimation failure. Combining Double Machine Learning and conformal prediction, the tool evaluates whether reliable conditions exist to adjust prices or if pausing the decision is preferable. When the system abstains, it exhaustively classifies the reason for the pause, identifying which products require designed pricing experiments or whether aggregating data to the brand level restores usable estimates. Tested on controlled synthetic data, the model shows that this operational discipline drastically reduces estimation error (lowering RMSE from 0.571 to 0.159) and offers a practical, secure alternative to blind estimation in thin-data retail environments.