Decomposition of a signal to slow and fast components
Your problem is pervasive in signal and image processing: being able to separate a trend (with some assumptions) from a signal (with some other assumptions) and potentially additional noise. This is an instance of (semi-)informed source separation: a sine from a drift,  a texture from a cartoon image, a biological information from instrumental artifacts, etc, that requires a more involved modeling and optimization framework.

Your choices are related to fundamental questions related to the model you can build based on physical and mathematical assumptions, aside from the algorithmic tools you can program.

1) Low-pass filtering: 
Standard low-pass filtering is linear and time-invariant. Linearity is very consistent with "signal = tonic + phasic". But phasic could be (on theory) negative, as "$2+(-1) = 1$". As your graphic does not include x/y scales, "above" is clear, but i am not sure of what you can model with "no points in the recorded signal should be below the tonic component", since additional noise can be fluctuating too. But positivity is a property which can be taken into account, with some non-linearity caveats.

2) Duration:
"that are at least as far as the duration of phasic response" : your assumption seems to me to related to a time-delay, yet i cannot interpret it precisely so far.

3) High-order derivatives: 
"A third approach would be to work on first or second derivative space and characterize the system in this domain, as it is supposed to be less sensitive to low-variations" Indeed, slow variations are expected to be with limited derivatives. This could result in a "sparsity" prior assumption.

4) Causality: "rely more on the history" there is some causality assumption behind that. This too is a tough stuff.

All your four approaches seem consistent to me, and may help you ponder and state your assumptions. They are otherwise termed as “morphological component analysis (MCA)”, “geometric separation” or “clustered sparsity”, adding positivity. Such topics are addressed for instance in "Blind source separation of positive and partially correlated data" http://dx.doi.org/10.1016/j.sigpro.2005.03.006. 

You may find additional history for instance in the introduction of (mong other papers "Chromatogram baseline estimation and denoising using sparsity (BEADS)" 
http://dx.doi.org/10.1016/j.chemolab.2014.09.014 
which also deals with positivity, low-pass-filtering and derivative sparisty, yet the causal approach is not considered. So you can specify your model in more precise terms (you did do talk about the noise yet), you may find some tracks to solve your questions