Marginal Structural Models for the Estimation of Direct and Indirect Effects
Bibliographic Data
| ID | 23367684 |
|---|---|
| Authors | Tyler J Vanderweele (0000-0002-6112-0239, Ford Foundation, corresponding author) |
| Year | 2009 |
| Volume | 20 |
| Issue | 1 |
| Pages | 18-26 |
| Publication date | 2009-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Epidemiology (JOURNAL) |
| Journal identifiers | ISSN: 1044-3983 • E-ISSN: 1531-5487 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/ede.0b013e31818f69ce |
| PMID | 19234398 |
| OpenAlex | W1993211119 |
| Language | EN |
| Citations received | 93 |
| References cited | 29 |
The estimation of controlled direct effects can be carried out by fitting a marginal structural model and using inverse probability of treatment weighting. To use marginal structural models to estimate natural direct and indirect effects, 2 marginal structural models can be used: 1 for the effects of the treatment and mediator on the outcome and 1 for the effect of the treatment on the mediator. Unlike marginal structural models typically used in epidemiologic research, the marginal structural models used to estimate natural direct and indirect effects are made conditional on the covariates.
Causal inference · Covariate · Econometrics · Economics · Estimation · Estimator · Indirect effect · Inverse probability weighting · Marginal distribution · Marginal model · Marginal structural model · Random variable · Regression analysis · Statistics · Weighting · Advanced Causal Inference Techniques · Mathematics · Medicine · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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| Unique citing works | 93 |
|---|---|
| Citations per year | 5,47 |
| Citation span | 2009 - 2026 (18) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 89 |