Causal ML for Data Science: Deep Learning with Instrumental Variables

Historically, both economists and philosophers have been preoccupied with extracting an understanding of cause and effect from empirical evidence. David Hume, an economist and philosopher, is renowned for exploring causality, both as an epistemological puzzle and as a matter of practical concern in applied economics. In an article titled "Causality in Economics and Econometrics", economics professor Kevin D. Hoover states, "economists inherited from Hume the sense that practical economics was essentially a causal science." (Hoover, 2006). As a capital "E" Empiricist, Hume was a major influence on the development of causality in economics; his skepticism created a tension between the "epistemological status of causal relations and their role in practical policy." (Hoover, 2006). In 1739, when Hume famously defined causation in the "Treatise of Human Nature", I doubt he would have been able to foresee the radical change wrought by the exponential progress of technological evolution. Nor could he have imagined our present-day reality where deep learning is used to determine cause and effect. Today, "causal science" is being driven by machine learning, however, it is still a nascent area and development has focused primarily on theory.