Clayton Browne, experienced writer summarizes, "Modern economics has masqueraded as a "science" for many decades now, but at its heart the discipline of economics
is based on words and theories, not numbers.
Economics as it is generally understood today began as a branch of philosophy in the 17th and 18th century, with famous political philosophers and theorists such as John Locke and David Hume laying the groundwork that Adam Smith and others would systematize and rigorize into classical economics. However you want to categorize economics as an academic discipline, make no mistake about it, unlike in physics, chemistry or biology, research in economics is not based on the scientific method.
Of interest, econometrics is branch of economics that applies mathematical methods (statistics) to describe economic systems.
Economics' math problem
Noah Smith of Bloomberg View says that the discipline of economics has a problem in the way it uses math. In his September 1st article, Smith argues it appears that modern economics has been trying to evolve in to a subfield of applied math, but also notes that "applied math disciplines -- computational biology, fluid dynamics, quantitative finance -- mathematical theories are always tied to the evidence. If a theory hasn’t been tested, it’s treated as pure conjecture."
Smith then goes on to point out that the theoretical framework of economics puts the cart in front of the horse in terms of applying the scientific method. "Traditionally, economists have put the facts in a subordinate role and theory in the driver’s seat. Plausible-sounding theories are believed to be true unless proven false, while empirical facts are often dismissed if they don’t make sense in the context of leading theories."...
Push towards machine learning reflects the new direction of economics
The surge of interest in machine learning in the field of economics is certainly related to the trend towards empiricism. Machine learning can be generally defined as a set of statistical data analysis techniques to identify key features of the data not using a specific theory. As Smith says: "...machine learning “lets the data speak.” He also highlights that with Big Data moving to the fore today, machine learning is suddenly a hot field, and has become a key tool in the exploding field of data science.