On June 20, we had a miniconference on causal inference at the Columbia University Statistics Department. The conference consisted of six talks and lots of discussion. One topic of discussion was the use of propensity scores in causal inference, specifically, discarding data based on propensity scores. Discarding data (e.g., discarding all control units whose propensity scores are outside the range of the propensity scores in the treated group) can reduce or eliminate extrapolation, a potential cause of bias if the treated and control groups have different distributions of background covariates. However, it’s sort of unappealing to throw out data, and can sometimes lead to treatment effect estimates for an ill-defined subset of the population. There was discussion on the extent to which modeling can be done using all available data without extrapolation. Other topics of discussion included bounds, intermediate outcomes, and treatment interactions. For more information, click here.