The Self-Correcting Budget: Removing an Allocator's Own Footprint from Attribution

Sameer Siddiqui

The Self-Correcting Budget: Removing an Allocator's Own Footprint from Attribution

Author(s)	Mr. Sameer Siddiqui
Country	India
Abstract	Automated advertising systems increasingly close the loop between measurement and action: an attribution model estimates channel contribution, and an optimizer reallocates budget toward the apparent contributors. When such a loop operates on raw attribution, it confounds the consequences of its own reallocation with the underlying merit of the channels it evaluates. A channel that harvests pre-existing demand appears more efficient precisely because the system spends into it, producing a self-reinforcing bias toward the least incremental channels. We characterize this failure mode from first principles using event-level data, and evaluate a mechanism that treats each reallocation as a randomized experiment the system performs on itself: a within-channel holdout supplies an incrementality estimate, and the debiased signal—rather than raw attribution—drives the next allocation. In a controlled simulation with known ground truth across forty randomized environments, the debiased loop reduces efficiency-estimation error, avoids the adversarial “trap” channel, and—most importantly—retains a near-constant share of optimal return as confounding strengthens, whereas the raw-signal loop degrades monotonically. We report magnitudes as illustrative of the mechanism rather than as benchmarks from live systems, and delineate the boundary between established incrementality measurement and the novel closure it enables.
Keywords	marketing attribution; incrementality; causal inference; budget allocation; closed-loop control; confounding
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-20

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The Self-Correcting Budget: Removing an Allocator's Own Footprint from Attribution

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