Decision Making
Robust Portfolio Optimization in Models with Diminishing Returns
In our last post, we discussed how model uncertainty poses a risk when allocating resources among productive assets. In this post, we expand the discussion to models with diminishing returns. Such models are common in economics. As before, we can incorporate model uncertainty directly into the problem, achieving good performance regardless of the true model, with minimal impact to nominal performance. Robust optimization is both powerful and practical.
Robust Portfolio Optimization in Generalized Linear Models
Often we run an A/B test in order to inform some decision. But every A/B test involves uncertainty, no matter the sample size. This uncertainty poses a risk to our decision, which can be hedged by a process analogous to diversifying an investment portfolio. Finding a robust-optimal portfolio is both practical and fast.
The Alternative to Causal Inference is Worse Causal Inference
Some of the most important questions data scientists investigate are causal questions. They’re also some of the hardest to answer! A well-designed A/B test often provides the cleanest answer, but when a test is infeasible, there are plenty of other causal inference techniques that may be useful. While not perfect, these techniques are much better than the alternative: ad hoc methods with no logical foundation.
Unshackle Yourself from Statistical Significance
Don’t be a prisoner to statistical significance. A/B testing should serve the business, not the other way around!
Three Goals of Statistics: Description, Prediction, and Prescription
The great successes of Machine Learning in recent years are based on our ability to extrapolate and predict based on data. The next big step is learning and leveraging the relationship between cause and effect to prescribe what action to take.
