Targeted maximum likelihood estimation
Targeted Maximum Likelihood Estimation (TMLE), also known as Targeted Minimum Loss-based Estimation, is a general framework for constructing estimators in statistical and causal inference settings. It was introduced by Mark J. van der Laan and Donald Rubin in 2006 and is particularly useful in machine learning, biostatistics, and epidemiology, where outcomes may depend on high-dimensional covariates and complex data structures.[1]
References
- ↑ Laan, M. J. van der, Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer series in statistics. Springer. ISBN 978-1-4419-9781-4. Search this book on
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