Why make this assumption? I'm hoping to see more justification as to why the main effect should be assumed to be exactly 2x the interaction effect.
Edit: In comments he wrote - I think it makes sense, where possible, to code variables in a regression so that the larger comparisons appear as main effects and the smaller comparisons appear as interactions.
The assumption that interactions are smaller than main effects is generally true in real life. There's an idea in statistical modeling called the hierarchy of effects. Main effects are the largest while second-level effects are smaller and don't appear without the presence of the corresponding main effects. It's possible to construct datasets in which the hierarchy of effects isn't true (synergy only, without any benefit from a single variable), but it's uncommon in real life that you could have an interaction without both corresponding main effects being present.
It's simply field experience that interactions are more typically subtler modifiers of strong main effects – here's a relevant quote from the second source:
>Unfortunately, very few times in psychology do we add a factor and expect a complete reversal of the effect. (...) [It’s] more usual that we expect the new condition to alter the size, rather than direction, of the existing effect.
Or from the first:
>[Whatever] power [simple effect study] had, at least twice as many subjects are needed in [interaction study], per cell, to maintain it. We know this because we are testing the reduction of that same effect.