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Consider three random variables $X,Y,Z$. It is standard that $I(X,Y|Z)=0$ if and only if $X,Y$ are conditionally independent given $Z$. If instead we require $I(X,Y|Z)=I(X,Y)$, what do we get? What properties would have the probability distributions that satisfy such constraint?

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You are in luck: this is a well-studied quantity under a different name. This is equivalent to the Interaction information or a measure of synergy being equal to zero. Consider the quantity $S(X,Y,Z) = I(X,Y|Z) - I(X,Y)$. In 2 this is identical to eq. 10 if you first re-write $I(X,Y|Z) = I(X, (Y,Z)) -I(X,Z)$, so that $S(X,Y,Z) = I(X, (Y,Z)) -I(X,Z) - I(X,Y)$. This quantity can be positive, negative or zero. If it is zero, it means that the information that $Y$ and $Z$ have about $X$ is additive. If it is negative it means that the information that $Y$ and $Z$ have about $X$ is redundant, and if it is positive the information is synergistic (i.e., Y and Z together have information about X that can not be gleaned from either one individually).

The implications for the probability distributions that satisfy this constraint are a little more complicated. In this paper, they relate the synergy score to maximum entropy distributions satisfying certain constraints. If this quantity is negative, it implies something about the existence of common ancestors according to this paper, but I don't think it has implications in your case.

Another way to look at this condition is using the Venn diagram here. Your condition is equivalent to the very center gray area having a zero (that's just the triple information/interaction information again).

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  • $\begingroup$ Great, thanks! So what distributions have the property that the interaction information is zero? Can we find such a distribution with given constraints, as we do for maximal entropy? $\endgroup$ – geodude Sep 15 '15 at 6:35
  • $\begingroup$ This is interesting, although there is a large literature about detecting synergistic information or redundant information, the special case right in the middle seems to be neglected! I hope someone else chimes in with something more direct and concrete. $\endgroup$ – Greg Ver Steeg Sep 15 '15 at 15:17
  • $\begingroup$ On the other hand, maybe the arxiv paper about max ent and "connected correlations" is all there is to it. Eq. 7 says that the probability distribution has to have a certain form as an exponential with pairwise functions of the variables only (i.e., no function of x,y, and z can appear). $\endgroup$ – Greg Ver Steeg Sep 15 '15 at 15:40
  • $\begingroup$ In what they call the FM case, $I=-1$ but the probability distribution has only pairwise functions, so not every pairwise potential has zero interaction information. But does the viceversa hold, i.e. does $I=0$ imply only (up to) pairwise functions? $\endgroup$ – geodude Sep 15 '15 at 15:54

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