  The Principle of insufficient Reason (PIR) assigns equal probabilities to
each alternative of a random experiment whenever there is no reason to prefer
one over the other. Maximum Entropy (MaxEnt) generalizes PIR to the case where
statistical information like expectations are given. It is known that both
principles result in paradox probability updates for joint distributions of
cause and effect. This is because constraints on the conditional P(effect |
cause) result in changes of P(cause) that assign higher probability to those
values of the cause that offer more options for the effect, suggesting
'intentional behaviour'. Earlier work therefore suggested sequentially
maximizing (conditional) entropy according to the causal order, but without
further justification apart from plausibility for toy examples. We justify
causal modifications of PIR and MaxEnt by separating constraints into
restrictions for the cause and restrictions for the mechanism that generates
the effect from the cause. We further sketch why Causal PIR also entails
'Information Geometric Causal Inference'.
  We briefly discuss problems of generalizing the causal version of MaxEnt to
arbitrary causal DAGs.
