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Found 7 papers, 6 papers with code
Synthetic Data: Can We Trust Statistical Estimators?
The increasing interest in data sharing makes synthetic data appealing.
Assumption-lean inference for generalised linear model parameters
These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified, but have the advantage that they continue to capture respectively the primary (conditional) association between two variables, or the degree to which two variables interact (in a statistical sense) in their effect on outcome, even when these models are misspecified.
Simulating longitudinal data from marginal structural models using the additive hazard model
Simulation studies are a key tool for this, but their use to evaluate causal inference methods has been limited.
Methodology
Confounder selection strategies targeting stable treatment effect estimators
For these stated reasons, confounder (or covariate) selection is commonly used to determine a subset of the available covariates that is sufficient for confounding adjustment.
Methodology
Non-linear Mediation Analysis with High-dimensional Mediators whose Causal Structure is Unknown
With multiple potential mediators on the causal pathway from a treatment to an outcome, we consider the problem of decomposing the effects along multiple possible causal path(s) through each distinct mediator.
Methodology
Heterogeneous Indirect Effects for Multiple Mediators using Interventional Effect Models
In this article, we introduce simplified estimation procedures for such heterogeneous interventional indirect effects using interventional effect models.
Methodology
Structural Nested Models and G-estimation: The Partially Realized Promise
Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures.
Methodology
