Managers often lower prices when they expect weak sales and raise them when demand is strong. Past prices then carry demand information the model may not see. Price and sales can rise together even when raising the price reduces demand. Fixed effects, SHAP and better forecast scores do not fix this on their own.
Prefer a randomised price experiment within a small approved grid, stratified by product, market and relevant calendar blocks. Run a concurrent current-policy control. Record assignment, realised exposure, overrides and outcomes. Define the primary effect as assignment to the pricing policy, so overrides cannot quietly change the population being compared. Fisher, Gallino and Li give a controlled retail example that uses randomised prices to address this endogeneity problem. Its reported improvement is specific to that setting, not a forecast for yours.
For observational estimation, write down an identification argument before choosing the estimator:
- Adjustment/DML: all common causes of price assignment and demand are recorded, enough price variation remains within comparable contexts, and treatment and outcome definitions are consistent. Avoid conditioning on anything the price change itself affects. DML cross-fitting reduces some estimation biases, but it cannot reveal missing confounders. The EconML DML documentation also sets out structural assumptions. A continuous-treatment causal forest estimates effects within those assumptions, and a local marginal effect is not automatically a complete nonlinear demand curve.
- Instrumental variables: the instrument changes price, is independent of unobserved demand shocks given the stated controls, and affects demand only through price. Report first-stage strength, the population and variation identified, and any extra functional-form or monotonicity assumptions. A competitor's price or your cost is not automatically a valid instrument, because common shocks, advertising and availability can create other paths to demand. EconML's instrument guide describes this distinction. A more recent competitive-pricing study, Jiang and Li, combines experiments and instrumental estimation in its own setting.
- No credible identification: use the model only as a forecast or analyst simulator, keep the
approved current policy, and design an experiment. Label counterfactual numbers as unvalidated
scenarios. Adding
CausalForestDMLto the code does not change that evidence gate.
For continuous price, support means real variation near the proposed price within comparable contexts, not just lying between the global minimum and maximum. If every item was always priced differently, cross-item variation cannot identify an item's own-price effect. Plot price variation by SKU, store and season, and show which decisions must abstain.