Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.

  title={Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies.},
  author={Megan S. Schuler and Sherri Rose},
  journal={American journal of epidemiology},
  volume={185 1},
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
Highly Cited
This paper has 22 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 68 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 12 extracted citations


Publications referenced by this paper.
Showing 1-10 of 51 references

Targeted Learning: Causal Inference for Observational and Experimental Data

MJ van der Laan, S. Rose
View 10 Excerpts
Highly Influenced

Mortality risk score prediction in an elderly population using machine learning.

American journal of epidemiology • 2013
View 5 Excerpts
Highly Influenced