Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies

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 me...

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Vydané v:American journal of epidemiology Ročník 185; číslo 1; s. 65
Hlavní autori: Schuler, Megan S, Rose, Sherri
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.01.2017
Predmet:
Bias
Causality
Computer Simulation
Confounding Factors (Epidemiology)
Data Interpretation, Statistical
Depression - psychology
Depression - therapy
Epidemiologic Research Design
Exercise - psychology
Humans
Likelihood Functions
Machine Learning
Observational Studies as Topic - methods
Observational Studies as Topic - standards
Observational Studies as Topic - statistics & numerical data
Propensity Score
targeted maximum likelihood estimation
causal inference
machine learning
observational studies
super learner
ISSN:1476-6256, 1476-6256
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Shrnutí: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 target parameter. Under standard causal assumptions, estimates can be interpreted as causal effects. Because TMLE has not been as widely implemented in epidemiologic research, we aim to provide an accessible presentation of TMLE for applied researchers. We give step-by-step instructions for using TMLE to estimate the average treatment effect in the context of an observational study. We discuss conceptual similarities and differences between TMLE and 2 common estimation approaches (G-computation and inverse probability weighting) and present findings on their relative performance using simulated data. Our simulation study compares methods under parametric regression misspecification; our results highlight TMLE's property of double robustness. Additionally, we discuss best practices for TMLE implementation, particularly the use of ensembled machine learning algorithms. Our simulation study demonstrates all methods using super learning, highlighting that incorporation of machine learning may outperform parametric regression in observational data settings.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kww165