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This article explains cooperative game theory concepts—Last‑On‑The‑Bus Value and Shapley Value—and demonstrates how they can be used to fairly allocate scores among group project members by evaluating each participant’s marginal contributions across all possible joining orders.
This article introduces the Shapley value concept, its fairness axioms, demonstrates its use in a profit‑allocation problem and in interpreting machine‑learning models with SHAP, and provides complete Python implementations for both cases.
CausalMTA introduces a causal‑inference‑based multi‑touch attribution framework that eliminates static user‑attribute bias through variational recurrent auto‑encoder re‑weighting and dynamic preference bias via a gradient‑reversal‑layer‑augmented RNN, producing an unbiased conversion predictor whose Shapley‑value credits outperform existing methods on synthetic, Criteo, and Alibaba datasets.
Effective marketing budget allocation relies on robust channel attribution models that combine dimensions, metrics, and segmentation with rule‑based or data‑driven (Shapley) credit assignment across defined attribution windows, enabling multi‑touch analysis, conversion‑time insights, and ROI‑focused channel performance evaluation.