answer-the-why-not-only-the-what

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Use when writing results and discussion, and when a task or a reviewer asks why an effect happens rather than whether it does. Covers the difference between reporting an effect and accounting for it, and what a mechanism claim needs behind it.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# A measured effect is half the result Plenty of the questions a study is judged on are not "what is the number" but "why is it that number": why two methods that should differ give the same gain, why an effect is larger in one regime, why the improvement vanishes past a threshold, what the failure cases have in common. A report can measure such an effect precisely and score nothing on it, because it never says what the effect is *evidence of*. Numbers are the input to that argument, not the argument. ## What a mechanism claim needs - **The candidate explanations, named.** Usually two or three are plausible. Say which they are before you argue for one; a single explanation asserted looks like the only one you thought of. - **Something that distinguishes them.** An ablation, a stratification, a regime where they predict different things. This is often cheap once the main result exists — you already have the pipeline. - **The one that survives, and what would overturn it.** A mechanism nothing could refute is a story. ## Where it goes In the Discussion, and again in one sentence in the Results next to the effect it explains. A reader who sees the number and no account of it forms their own, and it is rarely yours. ## When you cannot explain it Say that, and say what you would run. "We do not know why X and Y agree here; distinguishing the two accounts would need Z" is a real contribution and reads as honesty. Silence next to a surprising number reads as not ha...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
5 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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