Medicine (and also social sciences) is indeed more complex; but classification and prediction are still the basis for making treatment recommendations, for example.
Still, the task really is the same. A NN (like those that Torch, Theano, TensorFlow, and PyTorch produce; now with the ONNX standard for neural network model interchange) learns complex relations and really doesn't care about causality: minimize the error term. Recent progress in reducing the size of NN models e.g. for offline natural language classification on mobile devices has centered around identifying redundant neuronal connections ("from 100GB to just 0.5GB"). Reversing a NN into a far less complex symbolic model (with variable names) is not a new objective. NNs are being applied for feature selection, XGBoost wins many Kaggle competitions, and combinations thereof appear to be promising.
Actually testing second-order effects of evidence-based economic policy recommendations is certainly a complex highly-multivariate task (with unfortunate ideological digression that presumes a higher-order understanding based upon seeming truisms that are not at all validated given, in many instances, any data). A causal model may not be necessary or even reasonably explainable; and what objective dependent variables should we optimize for? Short term growth or long-term prosperity with environmental sustainability?
... "Please highly weight voluntary sustainability reporting metrics along with fundamentals" when making investments and policy decisions?
Were/are the World3 models causal? Many of their predictions have subsequently been validated. Are those policy recommendations (e.g. in "The Limits to Growth") even more applicable today, or do we need to add more labeled data and "Restart and Run All"?
...
From https://research.stlouisfed.org/useraccount/fredcast/faq/ :
> FREDcast™ is an interactive forecasting game in which players make forecasts for four economic releases: GDP, inflation, employment, and unemployment. All forecasts are for the current month—or current quarter in the case of GDP. Forecasts must be submitted by the 20th of the current month. For real GDP growth, players submit a forecast for current-quarter GDP each month during the current quarter. Forecasts for each of the four variables are scored for accuracy, and a total monthly score is obtained from these scores. Scores for each monthly forecast are based on the magnitude of the forecast error. These monthly scores are weighted over time and accumulated to give an overall performance.
> Higher scores reflect greater accuracy over time. Past months' performances are downweighted so that more-recent performance plays a larger part in the scoring.
The #GobalGoals Targets and Indicators may be our best set of variables to optimize for from 2015 through 2030; I suppose all of them are economic.