Calories-in-calories-out: dangerous tautology or just a model needing improvement

It seems that the battlelines about very low-carb vs moderate/pragmatic lower-carb diets have been drawn around the question of whether or not calories count or whether there is a macronutrient ratio silver bullet that allows one to 'eat as much as one wants'.   Personal experience seems to dictate which side of of this divide one falls.  To us the salient questions is whether the CICO model has predictive power. If it does, it has an important role in addressing the obesity problem; if it doesn't, it is at best a confusing factor and possibly can be a harmful behavioral influence.

(By 'predictive power' we mean that if one specifies intake in terms of total calories and estimates energy expenditure one could anticipate expected weight change over some defined period.  Note that we are not even requiring that caloric requirements must be 'theoretically derived; it is fair game to estimate these from idiosyncatic past experience; if there is a reliable way to do this that identifies, say, the right lookback period for estimation for a given predictive horizon, that would be valuable.) 

There are plenty of obvious reasons that the predictive power of the CICO model may be limited  in practical application. 
1. measurement error ( you may simply be unable to record accurately your energy intake and output) eg:
a) the reference data may be poor or hard to apply in reality (how exactly to quantify the marbled-ness of a  steak)?
b) there may be metabolic advantages to certain foods that are not captured in the caloric estimates. (This would mean that the inputs would need to be corrected or else the CICO model would be dimensionally over-reduced)
c) there might be metabolic advantages/disadvantages associated with consuming foods in different combinations-- or there may be other conditionality, ex hormonal levels, that complicate the picture.
d) difficulties in accurately measuring your energy content of your exercise both deliberate and background

2. timescale effects, for instance:
a) water retention (and maybe other effects) can dominate weight changes over short periods of time(days)  and these would not be predicted by CICO. Of course if one could measure WIWO from all sources then the CICO predictions could be corrected and still be useful for these short timescales.
b) over somewhat longer horizons, adjustment of base metabolic rate due to changes in body mass, composition, etc... occur and make it (much) more complicated to connect activity level with true energy expenditure.
c) other changes may occur (such as the oft-discussed keto-adaptation) that might exploit effects such as 1b in a time dependent fashion.
d) 'behavioral' impact or feedback that make it improbable that the model user will realize the initially specified inputs (e.g. 'I am going to eat 1200 calories and walk 10km/day for the next 21 days').

Of course these practical problems are irrelevant under metabolic ward conditions where, in principle, total intake and expediture may be measurable.  Those may studies may be the only way to properly isolate and resolve the existence and magnitude of 1b & 1c although other approaches may also be possible (see below).  But even armed with those answers, another very interesting challenge exists: whether the CICO model can be improved to be more useful, i.e. more predictive and with more true explanatory power for people living in the real world.

The CICO model would seem to have some validity in boundary cases: e.g. zero food intake, forced feeding. Personal experience indicates that predictability is possible when one has a very large weight surplus and intake is severely restricted. As one reaches more of an equilibrium measurement noise is harder to smooth over.  Thus it is compelling to think that a systematic approach to collecting a large amount of data from n=1 experimentation, with the right clustering of participants to span the space of personal attributes,
could lead to a more practical model where the user would:
Provide inputs:
0. input personal attributes and possibly history thereof
1. specify a time period
2. specify planned food intake (the dimensionality of this specification is a critical output of the study above)
3. specify exercise plan
Get outputs:
0. expected weight change
1.  uncertainty band.
(2. these outputs would provide some rigor to the concepts of 'overfeeding' and 'energy excess' for the desired time period.)

This may seem like a technocratic fantasy but if even incremental improvements to CICO can be achieved, rather than being a lightning rod for disagreement about dietary approach amongst the afficionados,  the model could be a constructive vehicle for improving nutritional guidance to a mass audience.