Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

@article{Schuler2017TargetedML,
  title={Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.},
  author={Megan S. Schuler and Sherri Rose},
  journal={American journal of epidemiology},
  year={2017},
  volume={185 1},
  pages={
          65-73
        }
}
Estimation of causal effects using observational data continues to grow in popularity in the epidemiologic literature. While many applications of causal effect estimation use propensity score methods or G-computation, targeted maximum likelihood estimation (TMLE) is a well-established alternative method with desirable statistical properties. TMLE is a doubly robust maximum-likelihood-based approach that includes a secondary "targeting" step that optimizes the bias-variance tradeoff for the… CONTINUE READING
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