T4

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

This reciprocal form is one proposed finite-market response family, not a universal CAC curve. Here x is cumulative captured demand in the same units as TAM. Estimate marginal incremental contribution in tested spend support. Distinguish saturation, changing demand, creative fatigue, delivery and rival reactions. Use channel experiments with time paths and spillover checks; do not extrapolate the asymptote as fact.

• Wei, Bencina, Nguyen, Ribeiro and Zelditch — From Prediction to Incrementality — 10 August 2026. Full preprint. Field intervention; full methods/limitations inspected. Eight-week member-randomized 50/50 LinkedIn comparison of causal scoring, exploration, constrained allocation and withholding versus propensity targeting, with a matched delivery envelope. The reported value lift concerns the complete policy. Static audience definitions and segment lineage protect assignment. Source: https://arxiv.org/html/2608.10182v1
• Li, Yuan, Yang, Chen and Song — Attributed But Not Incremental — 25 June 2026. Full preprint. Production calibration against 18 rounds of channel experiments across up to eight TikTok markets, with rolling forward evaluation and daily hierarchical correction. Source: https://arxiv.org/html/2606.26690v1
• Heusch — Structural Estimation of MMM Parameters from Geo Experiments — 21 August, revised 7 September 2026. Full v2. Synthetic structural study: 156-week fictional retailer, three channels, four four-week go-dark tests plus cooldown. Uses time paths to estimate effectiveness, carryover and saturation. Source: https://arxiv.org/html/2608.21128v2
• Filippou, Quach and Jha — Pseudo Incrementality Testing — 16 September 2026. Full preprint. Changepoint detection plus counterfactual forecasting, two GeoLift benchmarks and synthetic stress tests. Only one of three nominal 90% intervals covers truth in the stress test; weak controls yield large bias. Source: https://arxiv.org/html/2609.18257v1
• Dubé, Morozov, She and Tuchman — The Effects of Ads on Beliefs and Implications for Consumer Search — written 31 July, posted 7 August, BFI release 17 August 2026. Paper and full PDF. Preregistered experiment, 17,775 Prolific participants, simulated bookstore; randomized ads/content/price and separate belief/shopping samples avoid priming. Data are from 2025. Source: https://bfi.uchicago.edu/working-papers/the-effects-of-ads-on-beliefs-and-implications-for-consumer-search/

• Requires 0<=x<TAM, CAC_0>0 and beta>0; convexity is not guaranteed over the full domain for every beta. Parameters are unfit here. Other response families and endogenous budgets can fit observational data similarly.
• one platform, short experiment, no isolation of individual components, no autonomous B2B seller economics. C3/C5/C6.
• normalized confidential errors; child-level credit is allocated rather than independently causally identified. A later cannibalization decline is before/after deployment evidence, not a randomized business-impact estimate. Complementarity and spillover remain limitations. C3/C5.
• stable parameters, specified adstock/saturation, no spillover and tested spend support. It is not an empirical estimate of SaaS brand decay. C3/C5.

Estimate marginal incremental contribution in tested spend support. Distinguish saturation, changing demand, creative fatigue, delivery and rival reactions. Use channel experiments with time paths and spillover checks; do not extrapolate the asymptote as fact.