Thread by @rkatlady

🚨 New blog post (a series!)
An Illustrated Guide to Targeted Maximum Likelihood Estimation 🎯

Part 1: motivation for “targeting” an estimand for inference and why we can & should incorporate data-adaptive/machine learning models

🧵1/
khstats.com/blog/tmle/tuto… #causaltwitter

Part 2: step-by-step explanations of the TMLE algorithm for a binary exposure and outcome using words, equations, #rstats code, and colored boxes™️

This corresponds to a printable TMLE “cheat sheet”
khstats.com/blog/tmle/tuto…
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Part 3: The statistical properties of TMLE 🧚🏽‍♂️

Double robustness! Efficiency! Explanations plus a brief outline of why TMLE works & lots of references to learn more if you‘d like.

khstats.com/blog/tmle/tuto…
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Thanks to the coworkers who reviewed my art project, especially @ildiazm who answered ~1.2 zillion questions🙏🏼

Also thanks to @WATZILEI for helpful feedback. Check out his very thorough, little more technical, #rstats TMLE tutorial!
migariane.github.io/TMLE.nb.html

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I’m open to suggestions and/or hearing about sentences that could be clarified for an applied, beginner audience. Email (on blog) is the best way to let me know.

To whatever subset of people who are both very visual aaaand curious about TMLE... happy learning!
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