Causal relationships can go in reverse time. For instance, I know I will be hungry tomorrow so today I go to the grocery store. Tomorrow’s breakfast causes todays shopping.
Perhaps better not to think too much on this though.
Perhaps better not to think too much on this though.
The famous counterexample metaphor is farmyard turkeys. For months, for their entire lives, the turkeys observe the diligent farmer bringing food and water, providing for their every need so that they don't have to do anything. On the day before Thanksgiving, a turkey philosopher confidently declares that, based on this prior evidence, we can conclude that the farmer, their great benefactor, exists only to serve and further the interests of turkeydom.
Aristotle, of course, used a broader ontology of causes than is commonly used today, but there should be no confusion if we are careful to define what we mean. In particular, final causes are more commonly regarded as psychological attitudes, whereby if someone acts now to bring about a certain circumstance in the future, the circumstance is caused by what they were thinking prior to the action.
There is, likewise, no logically necessary reason to assume that that causal process of metabolism will continue to work tomorrow as it always has in the past, though it is indeed reasonable to prepare for that outcome as the most likely outcome.
If someone buys something to eat at breakfast tomorrow, but dies before then, there was no future event to cause the purchase.
I think time may be similar, in that while we seem to have an innate conscious experience/perception of ~absolute time and absolute reality, the way the subconscious mind implements the portion of reality it is the source of may be substantially different.
This is a logical inconsistency. Tomorrow's breakfast does not yet exist, only your "knowing" about it. Your thought has already happened today, therefore "knowing" you will be hungry is the true cause, not the potential for breakfast itself.
On top of that, merging their effects (in policy setting for example) sort of is a barrier to attributing causality. It forces a 2 dimensional signal to go to 1 dimension. Neural networks can backpropagate attribution through that, but only with the convention that this attribution is the marginal effect. And that's a complex problem: who would be responsible for a policy change when there's a population contributing long-term for 48%, but a short term population adds 4%. Which population is the real cause? the 48% or the 4%? Is there a right answer to this question?