Marginal Structural Models versus Structural nested Models as Tools for Causal inference

By James M. Robins. 2000

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Citations in the corpus

Fair Inference on Outcomes (2017, background): “An alternative would be to reparameter-ize the observed data likelihood to include the causal parameter corresponding to the discrimination PSE, in a way causal parameters have been added to the likelihood in structural nested mean models (Robins 1999).”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “While the NMDP model M1 is commonly the one assumed in the causal inference literature in the context of marginal structural model estimation (Robins, 2000; Robins et al., 2000) and DTRs (Chakraborty and Moodie, 2013; Murphy et al.”
A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. (2017, background): “MSMs are a popular class of causal models (Robins, 2000; Robins, Hernán, and Brumback, 2000).”
Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content (2010, methodology): “…to model misspeci cation and to retain the possibility of borrowing information across treatments and baseline covariates, we consider (following Robins, 1999) exible semiparametric models of the form m (z; gx) = hsem (z; x; ) + q (z) (12) where q ( ) is an unknown function of z only, is an…”
The Statistics of Causal Inference: A View from Political Methodology (2015, neutral): “There has been considerable focus on treatments that vary over time in the biostatistics literature, and I suspect time-varying treatments will eventually be a topic of interest in political science (Robins 1997, 1999).”
Marginal and Nested Structural Models Using Instrumental Variables (2010, methodology): “We propose marginal and nested structural models using IVs under assumptions A1–A2 and refer to them as marginal and nested structural IV models, in the spirit of marginal and nested structural models of Robins (1998, 1999a) under the assumption of no unmeasured confounding, that is, Yd ⊥ D”
Interactive Q-learning for Quantiles (2017, methodology): “Thus far, SNDMs have not been extended to estimate a regime that maximizes a quantile.”
Non‐parametric methods for doubly robust estimation of continuous treatment effects (2015, methodology): “Robins and Rotnitzky (2001) gave general discussion and considered density estimation with missing data, while van der Laan and Robins (1998), van der Laan and Yu (2001), and van der Vaart and van der Laan (2006) used the approach for current status survival analysis; Wang et al. (2010) used it impl”
The Propensity Score with Continuous Treatments (2005, background): “Robins (1998, 1999) and Robins, Hern an, and Brumback (2000), use a related approach.”
On Instrumental Variables Estimation of Causal Odds Ratios (2011, methodology): “We will therefore consider estimation of the conditional causal odds ratio under so-called logistic structural mean models (Robins, 2000; Vansteelandt and Goetghebeur, 2003; Robins and Rotnitzky, 2004), which impose parametric restrictions on the conditional causal odds ratio (4).”
Data-adaptive selection of the truncation level for Inverse-Probability-of-Treatment-Weighted estimators (2008, positive): “Marginal structural models (MSMs) offer a powerful approach to this problem and are rapidly becoming a standard tool in causal inference (Robins, 1999; Robins et al., 2000; Robins, 2000).”
The International Journal of Biostatistics (2017, positive): “In particular, double robust locally eﬃcient augmented IPCW-estimators have been developed (Robins and Rotnitzky (2001b), Robins and Rotnitzky (2001a), Robins et al. (2000b), Robins (2000a), van der Laan and Robins (2003), Neugebauer and van der Laan (2005), Yu and van der Laan (2003)).”
Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models (2013, background): “…structural models (MSMs), a class of models, that are used to model the marginal distribution of counterfactual variables, have been proposed to define the causal effect of a time-varying treatment in such challenging situations (Robins, 1997, 1999a,b; Robins et al., 2000; Robins and Hernán, 2008).”