“Internal Validity”: A blog about stuff
I was interested in learning how to apply the Callaway & Sant'Anna staggered difference-in-differences framework to my work. After reading several papers and watching the video by Sant'Anna, I wrote a short tutorial on how to apply this framework to a simulated data. The tutorial is located on my RPubs site.
This is a unique method that used the R “did” package, which is based on the paper by Callaway & Sant’Anna.
Interrupted time series analysis (ITSA) is a study design used to study the effects of an intervention across time. An important feature of the ITSA is the time when the intevention occurs. The time before and after the intervention are of interest because we want to visualize if the trends are similar or different. Additionally, we want to visualize the change immediately after the intervention is implemenated. I call this period the index date.
In this article, I’ll review the single-group ITSA and multiple groups ITSA. Then I’ll review how to perform an ITSA in Stata.
You can view the complete tutorial on my RPubs site.
Recently, my classmate asked me how to perform empirical Bayesian shrinkage, a form of estimation that tries to adjust your sample mean to the grand mean by incorporating more variables. I haven't done this as part of my regular work so I had to review my past class notes.
I forgot how useful empirical Bayes estimates were and wanted to document what I discovered. In my research, I discovered an informative guide by David Robinson who used baseball statistics as a motivating example to explain empirical Bayesian shrinkage on his blog.
In addition, Nicolas Lartillot wrote a great summary of empirical Bayes estimation and Stein's paradox on his blog.
