<b>When Shapley and Markov disagree, who's right?</b>

<b>When Shapley and Markov disagree, who's right?</b>

A question worth sitting with, because both are sold as the rigorous alternative to last-click. They are not interchangeable.

<b>What the methods actually do</b>

— Shapley value borrows from cooperative game theory: it asks how much each channel adds <i>on average across every possible ordering</i> of the channels in a path. It distributes credit by marginal contribution.
— Markov chains model the journey as transition probabilities between states. Credit comes from the <i>removal effect</i>: delete a channel, see how much the conversion probability drops.

<b>The nuance</b>

Shapley is order-agnostic by construction — it averages permutations, so it tends to flatten sequencing. Markov keeps the path structure, so a channel that's a frequent <i>bridge</i> between other touches scores higher than its raw frequency suggests. On the same dataset they routinely disagree by 15-30% on a given channel's share, especially for mid-funnel display and retargeting.

Neither is measuring causation. Both are sophisticated rules for splitting <i>observed</i> credit among channels that co-occur with conversions. A channel can earn high removal-effect credit purely because converters happen to pass through it — correlation dressed in matrix algebra.

<b>What to actually do</b>

— Run both. Where they agree, you have a robust read. Where they diverge sharply, that's your flag to investigate, not to average.
— Treat divergence on a channel as a hypothesis to test with a holdout, not a number to trust.

Bottom line for practitioners: model disagreement is signal, not noise. Use Shapley and Markov as a consistency check on each other, and reserve causal claims for experiments.