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Everything about Targeted maximum likelihood estimation

Statistical estimation framework for causal inference

Targeted Maximum Likelihood Estimation (TMLE) (also more accurately referred to as Targeted Minimum Loss-Based Estimation) is a general statistical estimation framework for causal inference and semiparametric models. TMLE combines ideas from maximum likelihood estimation, semiparametric efficiency theory, and machine learning. It was introduced by Mark J. van der Laan and colleagues in the mid-2000s as a method that yields asymptotically efficient plug-in estimators while allowing the use of flexible, data-adaptive algorithms such as ensemble machine learning for nuisance parameter estimation. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Targeted maximum likelihood estimationA 5-minute orientation
  2. Skim a recent paper: Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic reviewInfluential work since 2022
  3. Pick up a classic book: The Conspirators HierarchyThe most-reprinted book on the subject
  4. Read the landmark paper: Targeted Maximum Likelihood Estimation for Causal Inference in Observational StudiesCited 375 times
  5. Go sideways: Average treatment effectA closely related topic
  6. Go sideways: M-estimatorA closely related topic

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🏆 Landmark papers (most cited)

🆕 Influential since 2022

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