<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TMLE on MALF</title><link>https://maluque.netlify.app/tags/tmle/</link><description>Recent content in TMLE on MALF</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy; 2025-2026 Miguel Angel Luque-Fernandez</copyright><lastBuildDate>Wed, 27 Apr 2016 00:00:00 +0000</lastBuildDate><atom:link href="https://maluque.netlify.app/tags/tmle/index.xml" rel="self" type="application/rss+xml"/><item><title>Targeted Maximum Likelihood Estimation: A Tutorial for Applied Researchers</title><link>https://maluque.netlify.app/project/tmle/</link><pubDate>Wed, 27 Apr 2016 00:00:00 +0000</pubDate><guid>https://maluque.netlify.app/project/tmle/</guid><description>TMLE is a semiparametric doubly-robust method for &lt;strong>Causal Infernece&lt;/strong> that enhances correct model specification by allowing flexible estimation using non-parametric machine-learning methods and requires weaker assumptions than its competitors.</description></item></channel></rss>