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原文摘要
This paper presents a causal decision framework for list-price elasticities in intermediated retail channels, where estimates are confounded by promotions, competitor moves and shopkeeper pass-through. Rather than forcing a number when the evidence is ambiguous, the system treats abstention (Wait) as an active diagnostic output. Built on Double Machine Learning and conformal prediction, it evaluates whether conditions exist to act on a recommended price or whether withholding is preferable, and it states when: acting beats withholding exactly when the calibration slope of the true headroom on the recommended move exceeds one half, a condition that predicts the sign of the gain in 37 of 38 synthetic groups. When the system abstains, it classifies the cause and flags which products are candidates for a designed pricing experiment. Aggregating estimates to the brand or category level, where independent price designs average out, cuts the root mean squared error from 0.488 to 0.159, although aggregation alone does not make the intervals honest. Tested on synthetic data with known truth and on public scanner data, the study reports its costs: the system abstains on 52% to 87% of presentations that were identifiable, and a single-window estimate forecasts real price changes poorly (calibration slope 0.22). What it offers is a way to audit any abstention rule and to publish at the unit the evidence supports; no commercial panel or pilot was used, so it does not measure commercial uplift.