Unified Methods for Censored Longitudinal Data and Causality

By Mark J. van der Laan, James Robins. 2003

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Citations in the corpus

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “1 nonrandom treatment assignment in observational studies and noncompliance in randomized experiments (Robins and Rotnitzky, 2001; van der Laan and Robins, 2003).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, background): “nonrandom treatment assignment in observational studies and noncompliance in randomized experiments (Robins and Rotnitzky, 2001; van der Laan and Robins, 2003 ).”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, methodology): “Super learner plus targeted maximum likelihood estimation (SL + TMLE).”
Tree-based multivariate regression and density estimation with right-censored data (2004, methodology): “…62H99; 62N02; 62G99; 62G05; 62G07; 62G08; 62C99; 62P10 Keywords: CART; Censored data; Comparative genomic hybridization; Cross-validation; Density estimation; Loss function; Microarray; Model selection; Multivariate outcome; Prediction; Regression tree; Risk estimation; Survival analysis”
Targeted Maximum Likelihood Learning (2006, positive): “In Subsection 5.1 we focus on censored data models to make the comparison with the estimating function methodology in van der Laan and Robins (2003).”
Semiparametric Counterfactual Density Estimation (2021, background): “A related difference is that the CDF is pathwise differentiable in a nonparametric model, whereas the density function is not [Bickel et al., 1993, van der Laan and Robins, 2003].”
Estimation of the causal effects of time-varying exposures (2008, background): “Violations of (i) are discussed in Robins (1997a, 1998); of (ii) in Robins, Rotnitzky, and Zhao (1995), Robins (2003), and van der Laan and Robins (2003); of (iii) in Robins (1994, 1997a) and Hernán, Brumback, and Robins (2001, 2002); and of (iv) in Robins, Rotnitzky, and Scharfstein (1999) and…”
Handbook of Big Data (2016, background): “Although TMLE is a general algorithm for a wide range of parameters, we focus on its implementation for the G-computation estimand.”
A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. (2017, methodology): “The most widely used method involves inverse probability of treatment weighted (IPTW; Robins, Hernán, and Brumback, 2000) estimation or augmented IPTW estimation (Scharfstein and others, 1999; van der Laan and Robins, 2003).”
MINIMAX ESTIMATION OF A FUNCTIONAL ON A STRUCTURED (2016, background): “This property, termed “double robustness” in [30], makes that for first-order inference it suffices that one of the two parameters be estimated well.”