So far in this series, we’ve been been using ordinary least squares (OLS) to analyze and make causal inferences from our experimental data. Though OLS is an applied statistics workhorse and performs admirably in some cases, there are many contexts in which it’s just not appropriate. In medical trials, for example, many of the outcome variables are binary. Some typical examples are whether a participant still has the disease (coded 1) or not (coded 0), or whether a participant has died (co...
