Finding contributing attributes in classification problem Imagine if products are classified as "Hot" and "Normal" based on their total annual sales.
I want to detect what are the possible variables / attributes that make a product "hot", based on this knowledge we can "config" a new product (or even an old one) so that it becomes "hot".
Another use case: if we know products with attribute A will be "hot" only when condition C is present (A and C are included in the data ofc) then we will not roll out such products until condition C is present (like certain season, certain holidays).
What methods do you suggest for this problem?
My current thought: This is a classification problem. Make a decision tree, and then check what are the decision points / rules and pick those as the "cause" (of course only if those coincide with the domain knowledge).
Another idea: go with neural nets -- (can I find the contributing attributes using this?)
I appreciate if you give me some advice and some tips for approaching this problem.
Important: The goal is not to classify the new products (although that will be a side effect) the main goal is to detect the "causes" of being "hot". 
 A: Causation is a murky subject and probably the easiest area in all of statistics to arrive at wrong answers. To go to your example, say we find in the data that $A$ products only succeed when they are released during period $C$ as well. But to causally interpret that to mean $A$ products need $C$ to succeed, we at least need to know that whether or not an $A$ product is released during $C$ is effectively random with regards to "hotness". But you could very well be saving your best $A$ products for $C$ (say, a holiday period) and you're simply picking up on that and not the genuine effect of $C$, so that's a tough condition to swallow. 
I'd follow Ott Toomet's advice and start with a logistic regression (depending on your data, I think you could definitely overfit with a decision tree) and see what you can tease out. But ultimately, you need a heavy amount of domain knowledge of the area to carefully understand the results and even then, you're getting suggestive evidence, not conclusive. If you want to more solidly understand causality, you'd need to do something like a randomized experiment (i.e. randomly pick half your $A$ products to be in $C$ and half not in $C$ and then compare). 
Wish I had better answer, but causality is extraordinarily hard absent experimental data. 
