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Once Upon a Time Series
(This article appeared in biostatistics.ca and has been published here with permission. It is also published at Stats-of-1 here. Subscribe to Stats-of-1 for more n-of-1 posts and podcast episodes.)
In the summer of 2015, I started to feel hope. Not that I was in a bad place — not at all.
In four months, I would go on to defend my doctoral dissertation in biostatistics at a top-notch school of public health. I’d originally intended to focus on causal inference under the keen guidance and mentorship of Professors Michael Hudgens and Amy Herring.
Causal inference covers two broad areas. In experimental studies, some variable X is randomized or otherwise manipulated or assigned. This isn’t true in observational studies. The goal of causal inference in both is to guess the effect (if any) of X on some other variable Y.
I would eventually focus on the observational side. And six years later, I would invent a way to conduct causal inference using non-experimental time series data. But how did all of this happen?
Potential Fear of Missing Outcomes
Michael had pioneered an area of causal inference called interference. Given his expertise, we initially explored how to extend principal stratification methods. But after a year, we realized we didn’t have enough…
