Statistical Learning and Econometrics The principal goal of Econometrics is to find causal effects between economic variables. But the growing technology level seems to show us that Big Data and Statistical Learning result in a tradeoff with econometrics, because of according to some professors (and professionals),  the Statistics field, once is applied in economics, must be guide by economic theory because its alternatives find  correlations (Not inference).

Given the above, is it possible to find causal effects between economic variables with Statistical Learning methods as alternatives to Econometrics?

Thanks!

(Pardon forerrors, I'm not a native English speaker)
As @markleeds suggested, Judea Pearl made a significant contribution to the field of Causality.

His work (though not innovative) explains that causality cannot be represented by joint probabilities. In fact, even econometric models can't model causality.

So, we have to use Rubin's Causal model, Granger Causality, Difference-in-Differences or whatever, to explore causality.

So, the answer to your question is yes and no. We can use statistical learning techniques in combination with causal frameworks, but they alone can't provide causality rigorously.

For example, I came up with a very simple solution to use special impulse-responses (in my case, one-hot vectors like $(1,0,0), (0,1,0), (0,0,1)$) to clear out the partial effects of input variables to the outcome in Neural Nets. Link here: http://ravshansk.com/articles/irf-ann

But all of these are just hacks and don't quite have a rigor. Judea Pearl argues that we must have a new notation for causality or use graphical models whatsoever.