Causal inference is a valuable and evolving field that sits at the intersection of machine learning, statistics, and healthcare. But working across these disciplines can be challenging. I often found myself lost in translation, as the ML, statistics, and healthcare communities tend to use different terminology for the same concepts—or worse, the same terms for entirely different ideas. That confusion motivated me to start writing these blogs.
I don’t intend to teach causal inference from the ground up—there are already countless books and tutorials that do that well. Instead, my goal is to distill the key ideas behind seemingly complex biostatistical methods, and document the terms and insights I wish I had known when I began this journey two years ago.