Is the 'naive' linear attribution model actually the most honest one?

Is the 'naive' linear attribution model actually the most honest one?

The question: linear attribution splits credit equally across every touch in the path. It's dismissed as simplistic — the model you use when you've given up. But there's a contrarian case that it's the most intellectually honest default. Is it?

The argument for it: every weighted model — U-shaped, time-decay, even data-driven — encodes a claim about which touches mattered more. Linear makes the opposite, deliberately agnostic claim: we don't know, so we won't pretend. In the absence of a causal experiment, any non-uniform weighting is a hypothesis dressed as a result. Linear refuses to invent precision it doesn't have.

What the analysis shows: studies comparing models find linear is rarely the best fit to conversion data — but it's also rarely catastrophically wrong, and crucially it has no systematic directional bias toward openers or closers. Time-decay over-credits the bottom; first-touch over-credits the top; linear sits in the middle by construction. Its errors are diffuse rather than skewed.

The nuance: 'no bias' isn't 'accurate.' Linear is provably wrong whenever some touches genuinely matter more (they usually do). Its honesty is the honesty of a shrug — fine as a baseline, weak as a decision tool.

What to actually do: use linear as your reference point. When a fancier model disagrees with linear, ask whether the difference is evidence-backed or just the model's built-in prior talking.

Bottom line for practitioners: linear attribution is the null hypothesis of crediting. Don't ship it — but make every other model beat it on evidence, not on vibes.