I'm finally dipping my does into causal inference for quasi-experiments, and my first use case has missing data. In this post we practice propensity score matching with multiply-imputed data sets, and how to compute the average treatment effect for the treated (ATT) with g-computation.| A. Solomon Kurz
Sometimes in the methodological literature, models for continuous outcomes are presumed to use the Gaussian likelihood. In the sixth post of this series, we saw the gamma likelihood is a great alternative when your continuous data are restricted to positive values, such as in reaction times and bodyweight. In this ninth post, we practice making causal inferences with the beta likelihood for continuous data restricted within the range of \((0, 1)\).| A. Solomon Kurz
So far in this series, we have used the posttreatment scores as the dependent variables in our analyses. However, it’s not uncommon for researchers to frame their questions in terms of change from baseline with a change-score (aka gain score) analysis. The goal of this post is to investigate whether and when we can use change scores or change from baseline to make causal inferences. Spoiler: Yes, sometimes we can (with caveats).| A. Solomon Kurz
Books At present, all of my books share a similar format and goal. I am a fan of applied Bayesian statistics. In recent years, scholars have released user-friendly Bayesian software packages and have published reasonably-accessible introductory books on applied Bayesian analysis. These have been boons for us all. In my experience, Bayesian methods are easiest to use within the R computing environment by way of Paul Bürkner’s brms package. At present, very few textbooks showcase how to use ...| A. Solomon Kurz
| xcelab.net
