Dear Data Methods community,
I invite you to read this paper:
It introduces circular causality and an application for DCGs in Medicine. I’m happy to discuss the ideas and to help with data analysis procedures using DCGs as an abstraction in statistical models.
Best regards
This is excellent. Thank you. I very much enjoyed reading it.
The development of a vocabulary and mathematical characterization of causality in the “cyclic or reciprocation domain” is pivotal. I would like to learn more about DCGs.
Indeed substantially everything in biology is a reciprocation. I once offered students 100 dollars if they could identify a purely biological process that was not a reciprocation. (Although During pathology those reciprocations may have recoveries which are incomplete or fail all together). This causal characterization domain may be considered one level more fundamental than DAGs
Our early effort to address this included a vocabulary which embraced a “global time series matrix model” of the human. In that model we identify a “reciprocation” (the time series manifestion of a cycle) as a fundamental “integer of biology” . Reciprocations may be physiological or pathological. When they are physiological they become the baseline and perturbations of the cycles themselves project from that baseline as do recovery failures and incomplete recoveries of one or more cycles. .
By Objectifying the time series we get 5 primary fundamental time pattern types. From the paper
These are:
Perturbation- (a rise or fall away from the phenotypic or baseline cyclic or linear range)
Recovery (a rise or fall from a perturbation back toward baseline which follows a perturbation. )
Reciprocation (a perturbation followed by its recovery)
Distortion (a combination of perturbations induced by a common force such as a drug or invading organism)
Recovery from a Distortion (a combination of recoveries from the perturbations which comprise the distortion)
As mentioned, In the matrix model physiological (normal) cycles are the baseline in the matrix. Perturbations in that instance are perturbation of the physiological cyclic pattern. We can represent the cycles as a phenotypic linear baseline, that way perturbation of a baseline cyclic pattern and a baseline linear (non cyclic) pattern can be represented together in the same TS matrix.
The framwork that you mentioned seems nice, but it seems that the paper
that was linked is a different one.
Concerning the DCGs, I used differential equations and large samples for this paper,
but I intend to do something for small samples next. I am considering CRQA and symbolic regression for this (CRQA.jl + SymbolicRegression.jl)