Harvard’s Sendhil Mullainathan is one of a small number of economists who has delved into the world of machine learning, the subfield of computer science concerned with using algorithms to learn from data. His research, along with the work of Stanford’s Susan Athey, suggests that while machine learning may not revolutionize economics, it will greatly expand its possibilities, and more economists should be using it.
When economists analyze data, Mullainathan explains in a recent paper (pdf) with Harvard PhD student Jann Piess, they are typically trying to understand the causal relationship between two variables. For example, an economist might sift through real estate data to figure out how much the size, location, or other factors affect how much people are willing to pay for a home.
Chosen excerpts by Job Market Monitor. Read the whole story at Machine learning can help economists do their jobs better — Quartz