Does Correlation "Sometimes" Imply Causality? I am an MBA student taking courses in statistics.
I am sure that at some point, we have all heard the famous expression - "Correlation Does Not Imply Causality".
When we are being introduced to this concept, we are often shown an absurd example of two variables that are highly unlikely to have a causal relationship, yet seemingly appear to be correlated with one another. For example - it is highly unlikely that there might be a causal relationship between the "letters in the words of spelling bees" and the "number of people killed every year by spiders":

However, there are other examples in which it might be reasonable to assume that correlation does in fact imply causality. For example, it is quite likely that there might be some relationship between "life expectancy" and "GDP" - countries with higher GDP likely have stronger economies, stronger economies likely have stronger health sectors, countries with stronger health sectors likely have more hospitals/doctors ... using this logic, although there might be outliers and exceptions, it does not seem completely unreasonable to expect that there might be some causal correlation between income and life expectancy:

This leads me to my question - when we are told that "Correlation Does Not Imply Causality", does this statement indirectly mean that there might be some instances in which "Correlation DOES Imply Causality"?
For instance, suppose I have some data (for different countries) which shows a clear linear relationship between  "the number of doctors per 1000 people" and the "life expectancy" of those countries (i.e. people who live in countries with more doctors tend to live longer on average) - although there some philosophical arguments that suggest we might never be able to "truly" attribute causality between two concepts (e.g. perhaps tomorrow we find out that countries with more doctors have some newly discovered vitamins in their water and this is why they live longer, not actually due to more available doctors, yet this is highly unlikely) - would be fundamentally wrong and morally unreasonable to prematurely suggest that "there theoretically might exist some level of causal relationship between the number of doctors and life expectancy"?

Spurious relationship
"Correlation Does Not Imply Causality" This saying relates in particular to the idea that correlation between two variables does not imply a causal relationship between the two variables. It does not always mean that there is an absence of causality.
A typical example of the 'Correlation Does Not Imply Causality' is the presence of a confounding variable and is explained by the correlation between 'ice cream sales' and 'shark attacks'. The two are correlated, but the one does not cause the other. Yet, there are causal relationships present. Both are caused by hot weather which makes more people buy ice cream and more people go to the beach.
This is your situation with the map of GDP per Capita and Life expectancy. There is most likely some sort of causal relationship present in that situation. The problem is however that the correlation alone does not allow you to determine what type of causal relationship is present. The phrase "Correlation Does Not Imply Causality" or the term spurious relationship relates to the situation where somebody falsely/falliciously concludes that there is a particular causal relationship based on a correlation.
Spurious correlation
Your case of the relationship between scripps and spiders is of a more specific nature and sometimes referred to as spurious correlation (see Misunderstandings of "spurious correlation"?). It means that one falsely assumes that there is a statistical correlation where there is none.
Examples may occur when data are correlated (e.g. time series Why do these time series appear to be dependent?) or when people make multiple comparisons without correcting.
