This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social … adage ”once you understand OLS, you can work your way up to any other estimator,” and apply it to Machine Learning. Focusing … and fire some trailblazers to the forefront of AI and causal machine learning. …

antibiotic misuse, a leading driver of antibiotic resistance. We train a machine learning algorithm on administrative and … until test results are known and give them instantly to others. We find that machine learning can reduce antibiotic use by 7 …

challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a … framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in … learning predictions with physician diagnostic skill achieves a 25.4 percent reduction in prescribing and the largest welfare …

infinite-horizon learning, and subjective expectations with Euler-equation learning. Under rational expectations, the model … necessitates of large, possibly unrealistically so, degrees of myopia. The same result persists under infinite-horizon learning …, given that agents are still remarkably far-sighted. But, under Euler-equation learning, the model can fit the data with only …

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the … macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the … sentiment, defined as the degrees of excess optimism and pessimism that cannot be justified by the near-rational learning model …