Using scatterplot before running a controlled experiment to test causality This question is an extension of awesome thread Does causation imply correlation?.
As an example, let's say that I am running an online experiment on Stackoverflow to see whether asking more questions leads to an increase in time spent on stackoverflow. For this case, I would have historical data on # of questions asked over, say, 1 week and average time spent on stackoverflow. I can plot a scatterchart for the last two years and see the the relationship.
I have three interrelated questions:
Question 1: Before running an experiment, is it a good practice to plot a scatterchart between # of questions asked in 1 week and average time spent on stackoverflow? 
Question 2: If we do decide to plot the chart, one could argue that this scatterchart will show me association between the two variables. If correlation (or association) doesn't imply causation, what's the need for plotting such a chart? It will be great if someone can talk about the value of plotting scatterchart before running an experiment. 
Question 3: I learned from the Stackoverflow thread above that Causation doesn't imply correlation, but it does imply association. Hence, I'd believe that association (and not correlation) is a necessary condition for causation. If this is true, the benefit of scatterplot would be to check the necessary condition for causality. For instance, what if, I found from scatterplot that an increase in # of questions asked over a period of time is associated with a decrease in time spent on SO. This would mean that there is no causality. Hence, I'd not have to run an experiment. Am I correct?
I am new to the field of experimentation and causal-checks. I don't have a Ph.D. in econometrics or statistics. I took advanced graduate courses in Statistics long time ago. So, please excuse my ignorance. I'd appreciate if someone can guide me. Many thanks.
 A: 
Before running an experiment, is it a good practice to plot a scatterchart between # of questions asked in 1 week and average time spent on stackoverflow?

It is a very good idea to plot your data because it will give you an idea of what the association looks like. Visualisation is a great general tool in an analyst's chest, and where you have only 2 variables, a scatterplot can be very useful.

If we do decide to plot the chart, one could argue that this scatterchart will show me association between the two variables. If correlation (or association) doesn't imply causation, what's the need for plotting such a chart? It will be great if someone can talk about the value of plotting scatterchart before running an experiment.

One of the points of uncovering an association prior to performing an experiment is that you might find that the association is nonlinear and this will inform the type of model you want to fit. As you can see from the figure in one of the answers to the question you linked to, that of Anscombe's Quartet, it is possible for correlation to be zero yet a profound association to exist. If you simply analysed those data with correlation or linear regression you would be making a big mistake.

I learned from the Stackoverflow thread above that Causation doesn't imply correlation, but it does imply association. Hence, I'd believe that association (and not correlation) is a necessary condition for causation. If this is true, the benefit of scatterplot would be to check the necessary condition for causality. For instance, what if, I found from scatterplot that an increase in # of questions asked over a period of time is associated with a decrease in time spent on SO. This would mean that there is no causality. Hence, I'd not have to run an experiment. Am I correct?

Association is not a necessary condition for causation because there may be suppression. For example, if A truly causes B, but M also causes B (with an effect in the opposite direction to that of A, and A also causes M, then we can easily observe no association between A and B. For example, in R:
> N <- 1000
> set.seed(15)
> A <- rnorm(N)
> M <- A + rnorm(N)
> B <- A - M + rnorm(N)

> cor(A, B)
[1] 0.02033604

> plot(A, B)


