Causal inference is one of the most important topics in finance today. There is a difference between what correlates with or is associated with X and Y and saying that X causes Y. We can thank the work of Judea Pearl for truly focusing our attention on causality rather than correlation.
You should not ask what tends to happen to Y when X is high. Of course, you can ask, but that only refers to the association. The real question for causality is, "What will happen to Y if we set X to a specific vlaue and all other factros are held constant?". To answer that question, we have to consider the relationship between X and Y, and also ask what other factors may influence Y, such as variable Z. Does Z cause X, which then affects Y? Does Z affect Y directly? This type of thinking is not about fitting a set of past data into a relational model, but about asking the primary question of whether there is a reasonable link between these variables.
Before you run a statistical test, think about causal relationships and how they may be linked together. What type of relationship are you trying to find?
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