Statistical Modeling, Causal Inference, and Social Science

Regression models are often used, explicitly or implicitly, for decision making. However, the choices made in setting up the models (e.g., inclusion of predictors based on statistical significance) do not map directly into decision procedures. Bayesian inference works more naturally with decision analysis but presents problems in practice when noninformative prior distributions are used with sparse data. We do not attempt to provide a general solution to this problem, but rather present an application of a decision problem in which inferences from a regression model are used to estimate costs and benefits. Our example is a reanalysis of a recent meta-analysis of incentives for reducing survey nonresponse. We then apply the results of our fitted model to the New York City Social Indicators Survey, a biennial telephone survey with a high nonresponse rate. We consider the balance of estimated costs, cost savings, and response rate for different choices of incentives. The explicit analysis of the decision problem reveals the importance of interactions in the fitted regression model.

It was our attempt to perform a full decision analysis, rather than simply looking at some regression coefficients.

P.S. Yes, the tables should be graphs. Especially Table 2.

P.P.S. I don't know what's up with Hailin Lou.

P.P.P.S. As a former resident of the D.C. suburbs, I don't share Kevin's enthusiasm for a plan to add lanes to the Beltway.