Questions tagged [targeted-maximum-likelihood]
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Covariate balance diagnostics using TMLE function in R
I am currently using the tmle function in R to perform targeted maximum likelihood estimation as a doubly-robust alternative to propensity score methods in identifying an average treatment effect. ...
Targeted Learning in Data Science: background material
I am interested in the use of modern causal inference methods to research the association between a (non-genetic) exposure, and endogenous molecules and/or health outcomes (high-dimensional data). I ...
What statistical methodologies are currently used in clinical trials to estimate treatment effects?
For example, are more novel methodologies for obtaining unbiased treatment effect estimates, like Targeted Maximum Likelihood Estimation, being utilized in randomized controlled experiments in ...
Can Targeted Maximum Likelihood Estimation find the Average Treatment Effect on the Treated (ATT) instead of the Average Treatment Effect (ATE)?
Targeted Maximum Likelihood Estimation (TMLE) has a number of advantages for counterfactual modeling, however in my field we often want to estimate the ATT, rather than the ATE, for health care ...
Theory behind Targeted Maximum Likelihood Estimation (TMLE)
There are many fine how-to articles describing how to implement TMLE but they avoid the details of the underlying theory. I'm currently working my way through Targeted Learning: Causal Inference for ...
Targeted Maximum Likelihood Estimation for dummies?
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 ...
Are all models useless? Is any exact model possible -- or useful?
This question has been festering in my mind for over a month. The February 2015 issue of Amstat News contains an article by Berkeley Professor Mark van der Laan that scolds people for using inexact ...
What is "Targeted Maximum Likelihood Expectation"?
I'm trying to understand some papers by Mark van der Laan. He's a theoretical statistician at Berkeley working on problems overlap significantly with machine learning. One problem for me (besides ...