The is the third and final post in a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). In this section, I discuss more statistical properties of TMLE, offer a brief explanation for the theory behind TMLE, and provide resources for learning more.
Properties of TMLE 📈 To reiterate a point from Parts I and II, a main motivation for TMLE is that it allows the use of machine learning algorithms while still yielding asymptotic properties for inference.

The second post of a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). This section walks through the TMLE algorithm for the mean difference in outcomes for a binary treatment and binary outcome. This post is an expansion of a printable “visual guide” available on my Github. I hope it helps analysts who feel out-of-practice reading mathematical notation follow along with the TMLE algorithm.

The introductory post of a three-part series to help beginners and/or visual learners understand Targeted Maximum Likelihood Estimation (TMLE). This section contains a brief overview of the targeted learning framework and motivation for semiparametric estimation methods for inference, including causal inference.
Table of Contents This blog post series has three parts:
Part I: Motivation TMLE in three sentences 🎯 An Analyst’s Motivation for Learning TMLE 👩🏼💻 Is TMLE Causal Inference?

A condensed key for my corresponding TMLE tutorial blog post.
Initial set up Estimand of interest:
\[ATE = \Psi = E_W[\mathrm{E}[Y|A=1,\mathbf{W}] - \mathrm{E}[Y|A=0,\mathbf{W}]]\]
Step 1: Estimate the Outcome First, estimate the expected value of the outcome using treatment and confounders as predictors.
\[Q(A,\mathbf{W}) = \mathrm{E}[Y|A,\mathbf{W}]\] Then use that fit to obtain estimates of the expected outcome under varying three different treatment conditions:

Powered by the Academic theme for Hugo.