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I have tried to get my head around the concept of TMLE, but most references seem to be written by people who despise being understood (or maybe I am just hebetudinous). I have tried to read the paper

Targeted Maximum Likelihood Estimation: A Gentle Introduction

which is not as gentle as the title implies, in my opinion. Is there a reference that explains this approach at a, say university/Masters level? Or is the concept of TMLE much more complex that cannot be explained at this level?

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    $\begingroup$ I share your view that we much need an understandable intro to TMLE. And realism when it comes to its computational burden. $\endgroup$
    – Frank Harrell
    May 9, 2019 at 11:19

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I have found this paper to be more approachable: https://academic.oup.com/aje/article/185/1/65/2662306

Are you familiar with inverse probability weighting and plug-in estimators (e.g. the g-formula)? TMLE is essentially a way to merge these two estimators into a single estimator. I have found it helpful to have a good understanding of those before trying to approach TMLE.

This paper does a good job as an introduction to the g-formula: https://www.ncbi.nlm.nih.gov/pubmed/21415029

For inverse probability weights, I would recommend (despite it being a tad dense): https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2014/11/Marginal_Structural_Models_and_Causal_Inference_in.11.pdf

This paper on double-robustness for missing data does not directly describe TMLE, but I found it to be useful for a conceptual understanding: https://statnav.files.wordpress.com/2017/10/doublerobustness-preprint.pdf

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