Causal inference with generalized structural mean models

By Stijn Vansteelandt, Els Goetghebeur. 2003

193 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fb1c359503f6779a41d7e00efe4dea1e6453c093

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Citations in the corpus

Instrumental Variables: An Econometrician's Perspective (2014, unknown): “…Robins and Greenland (1996), Balke and Pearl (1995, 1997), Greenland (2000), Hernán and Robins (2006), Robins (1994), Robins and Rotnitzky (2004), Vansteelandt and Goetghebeur (2003), Vansteelandt et al. (2011), Hirano et al. (2000), Tan (2006, 2010), Abadie (2002, 2003), Duflo, Glennester and…”
Marginal and Nested Structural Models Using Instrumental Variables (2010, methodology): “For the logit link , this strategy is similar to that of Vansteelandt and Goetghebeur (2003) for model (4), and hence has the drawback that a misspecified model (5) can be incompatible with model (7).”
On Instrumental Variables Estimation of Causal Odds Ratios (2011, methodology): “Remember that, although Y may well depend on Z (in the presence of an exposure effect), the IV assumptions imply that Y (0) ⊥⊥ Z|C. Vansteelandt and Goetghebeur (2003) make use of this to obtain a consistent estimator of ψ∗ in model (8), which is chosen to make this independence happen.”
A review of instrumental variable estimators for Mendelian randomization (2015, background): “1.(90) Unfortunately, a logistic SMM cannot be defined in the same way, as the moment conditions in the estimating equations depend on unmeasured covariates due to the non-collapsibility of the odds ratio.”
Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome (2006, background): “Extensions to binary outcomes whose consistency depends on parametric assumptions are available (Robins and Rotnitzky, 2004; Vansteelandt and Goetghebeur, 2003).”
Structural nested models and G-estimation: the partially realized promise (2014, methodology): “…immediately to outcomes that do not lend themselves to linear modeling, for example, censored failure-time outcomes (Robins and Tsiatis (1991)) and dichotomous outcomes (Vansteelandt and Goetghebeur (2003); Robins and Rotnitzky (2004)), as well as to sequential treatments (Robins and Hernan, 2009).”
DOUBLY ROBUST INSTRUMENTAL VARIABLE REGRESSION (2012, methodology): “Also see Tan (2010b) for doubly robust estimators in marginal and nested structural models as extensions of Robins (1994) and Vansteelandt and Goetghebeur (2003), and see Van der Laan, Hubbard, and Jewell (2007) for doubly robust estimators of alternative causal paramters in randomized trials with n”