TMLE
Targeted maximum likelihood estimation (TMLE) is a general framework for estimating causal parameters in semi-parametric models. It blends data-adaptive machine learning with principled statistical inference to yield estimates that are consistent when either the outcome model or the treatment mechanism is correctly specified, and that are asymptotically efficient under regularity conditions. TMLE is widely used in epidemiology, biostatistics, and causal inference to estimate quantities such as average treatment effects, risk differences, and causal risks from observational data.
Conceptually, TMLE starts with an initial estimate of the outcome regression Q0(W) = E[Y|A,W] and, when needed,
The final parameter estimate is obtained by plugging Q*(W) into the estimating equation for the causal parameter.
Applications include estimating average treatment effects and other causal quantities from observational data with adjustment for