Auditing Marketing Budget Allocation with Hindsight Regret

Battle Count: 65

The paper is primarily about marketing budget allocation auditing, not financial trading. However, there are methodological parallels: (1) the regret framework conceptually resembles evaluating realized portfolio allocations against hindsight-optimal allocations; (2) constrained optimization under operational guardrails mirrors portfolio constraints; (3) uncertainty-aware evaluation via Monte Carlo is relevant to risk assessment; (4) the trade-off between allocation flexibility and detectability has analogies in rebalancing frequency decisions. The structural causal modeling and counterfactual evaluation approaches could inform post-hoc evaluation of trading strategy allocations. However, the domain, data structures, and decision contexts are substantially different from quantitative trading.

Implementation requires: (1) fitting a grey-box model combining parametric saturation curves with Gaussian Process mean and variance components (Matérn 2.5 kernels), with drift regularization across epochs; (2) solving constrained optimization problems (nonlinear, with epoch-to-epoch stability constraints) for oracle allocations; (3) implementing Monte Carlo uncertainty propagation with GP-consistent mean perturbations and heteroskedastic noise; (4) support-aware extrapolation with isotonic regression and epistemic variance inflation; (5) computing regret distributions and summary statistics. The combination of GP fitting, constrained nonlinear optimization, and Monte Carlo simulation makes this moderately to highly complex, though each component is individually well-understood.

The methodology is described in mathematical detail with clear assumptions (A1-A3), model specifications (Eq. 13-15), and algorithmic steps. However, the paper uses proprietary real marketing allocation logs from Expedia Group with confidentiality restrictions (portfolio composition, asset identities, absolute magnitudes omitted). No code or synthetic data is released. Reproduction would require access to similar proprietary allocation logs and implementation of the grey-box GP model, constrained optimizer, and Monte Carlo pipeline from scratch.