Сравнение на методи

	Targeted Maximum Likelihood Estimation (Epidemiology)	E-Value Sensitivity Analysis
Област	Social Epidemiology	Social Epidemiology
Семейство	Machine learning	Process / pipeline
Година на възникване	2006	2017
Създател	Mark J. van der Laan & Daniel Rubin; Megan Schuler & Sherri Rose (epidemiology tutorial)	Tyler J. VanderWeele & Peng Ding
Тип	Doubly-robust substitution estimator with a targeting update and machine-learning nuisance models	Assumption-free sensitivity analysis for unmeasured confounding
Основополагащ източник	van der Laan, M. J., & Rubin, D. (2006). Targeted maximum likelihood learning. The International Journal of Biostatistics, 2(1), Article 11.	VanderWeele, T. J., & Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.
Други названия	TMLE, Targeted Minimum Loss-Based Estimation, Doubly-Robust TMLE, Targeted Learning	E-Value, E-Value for Unmeasured Confounding, VanderWeele-Ding E-Value, Bias Factor Sensitivity Analysis
Свързани	3	3
Резюме	Targeted maximum likelihood estimation (TMLE), introduced by Mark van der Laan and Daniel Rubin in 2006, is a doubly-robust, semiparametric framework for estimating causal effects that marries machine learning with the theory of efficient influence functions. It begins by flexibly estimating two nuisance quantities — the outcome regression and the propensity score — typically with an ensemble 'super learner,' and then performs a clever targeting step that nudges the outcome model in exactly the direction needed to remove plug-in bias for the causal parameter of interest. The result is a substitution estimator that is consistent if either the outcome model or the propensity model is correct (double robustness) and asymptotically efficient if both are, all while permitting aggressive data-adaptive estimation. Schuler and Rose's 2017 American Journal of Epidemiology tutorial brought TMLE to a broad epidemiologic audience, including social-epidemiologic applications where confounding structures are complex and functional forms unknown.	The E-value, introduced by Tyler VanderWeele and Peng Ding in 2017, is a simple, assumption-free way to quantify how robust an observational association is to unmeasured confounding. It answers a single, sharply posed question: how strong would an unmeasured confounder have to be — in its association with both the exposure and the outcome — to fully explain away the observed effect? The larger the E-value, the more powerful a hidden confounder would need to be, and so the more robust the finding. The method rests on the bounding factor derived by Ding and VanderWeele in their 2016 'Sensitivity analysis without assumptions,' which holds regardless of the distribution or number of unmeasured confounders. Because it requires only the point estimate and confidence limit on the risk-ratio scale and no untestable bias parameters, the E-value has become a routine reporting standard in observational epidemiology, including social epidemiology where unmeasured confounding is pervasive.