Causal Inference and Social Networks: How to Quantify the Effects of our Peers
“If all of your friends jumped off a cliff, would you jump too?” While this comeback may be just an annoying retort to many teenagers, it presents an interesting question – what is the effect of social influence? This is what Ogburn, Sofrygin, Diaz, and van der Laan explore in their paper, “Causal Inference for Social Network Data”. More specifically, they are interested in developing methods to estimate causal effects in social networks and applying this to data from the Framingham Heart Study.
How to Learn about Housing Dynamics when You Don’t Have Housing Data
Data surrounds us in many aspects of our lives. We look at ratings on Amazon to determine whether to buy a product. We use Fitbits to track our step count. We browse Netflix recommendations generated using our streaming history. Everywhere, decisions are being made from numbers and data. However, while it seems like we can get data on anything, some datasets are much easier to collect than others.
Navigating the “Black Hole of Statistics”: Model Selection
A statistical toolbox in some ways is like an endless buffet. There are tons of statistical methods out there, ranging from linear models to statistical tests to neural networks. In addition, with increasing amounts of data, new applications from other fields, and increased computational power, methods are constantly being created or improved upon. Having so many possibilities, of course, has its perks. But researchers inevitably must face this daunting question: what method do you choose and why?