Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

By J. Lunceford, Marie Davidian. 2004

1,743 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Balanced Policy Evaluation and Learning (2017, methodology): “Based on the fact that 1 = E[⇡T (X)/'T (X)], a normalized IPW (NIPW) estimator is given by normalizing the weights so they sum to n, a common practice in causal effect estimation [2, 31]: ⌧̂NIPW(⇡) = ⌧̂WNIPW(⇡), W NIPW i (⇡) = W IPW i (⇡)/ Pn i0=1 W IPW i0 (⇡).”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, methodology): “The efficiency of these estimators can be further increased by adding covariates to the π-model that are predictive of yi, even if these covariates are unrelated to ti (Lunceford and Davidian, 2004).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, methodology): “The efficiency of these estimators can be further increased by adding covariates to the �-model that are predictive of yi, even if these covariates are unrelated to ti ( Lunceford and Davidian, 2004 ).”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, neutral): “Inverse probabilityeweighted marginal structural models [42,43] and some doubly robust models [44,45] similarly use propensity scores for reweighting observations to eliminate confounding and selection bias in observational settings.”
Covariate balancing propensity score by tailored loss functions (2016, background): “Lunceford, J. K. and M. Davidian (2004).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, neutral): “sity score (Lunceford and Davidian, 2004),”
Entropy Balancing is Doubly Robust (2015, methodology): “Conventionally, the recipe given by Robins and his coauthors is to fit separate models for propensity score and outcome regression and then combine them by a doubly robust estimator (see e.g. Robins et al., 1994; Lunceford and Davidian, 2004; Bang and Robins, 2005; Kang and Schafer, 2007).”
Longitudinal Data Analysis. Handbooks of Modern Statistical Methods (2009, background): “…hand, if the model for missingness is correctly specified, the augmentation term does not need to be correctly specified to yield consistent estimators Rotnitzky, 2001; van der Laan and Robins, 2003; Bang and Robins, 2005; see also Lipsitz, Ibrahim, and Zhao, 1999; Lunceford and Davidian, 2004).”
Accurate Inference for Adaptive Linear Models (2017, background): “Some of the work on learning from logged data also builds on propensity scores and their estimation [Imbens, 2000, Lunceford and Davidian, 2004], which are well-studied in econometrics and causal inference.”
Nonparametric estimation of causal heterogeneity under high-dimensional confounding (2019, result): “In line with other results in the literature on average effects (Hirano, Imbens, and Ridder, 2003, Robins, Rotnitzky, and Zhao, 1994, Lunceford and Davidian, 2004), they show that the variance for Inverse Probability Weighting (IPW) estimators can be substantially decreased when the propensity…”
Doubly Robust Inference With Nonprobability Survey Samples (2018, methodology): “The approaches described in Rivers (2007), Lee (2006), Lee and Valliant (2009) and Brick (2015) attempt to estimate π̃ i = P (R̃i = 1 | xi), where R̃i = 1 if i ∈ SA and R̃i = 0 if i ∈ SB.”
Comparison of Propensity Score Methods and Covariate Adjustment: Evaluation in 4 Cardiovascular Studies. (2016, methodology): “Some have advocated it as a preferred method (28,29).”
Covariate balancing propensity score (2014, background): “Imbens (2004), Lunceford and Davidian (2004) and Stuart (2010) have provided comprehensive reviews of these and other methods.”
DOUBLY ROBUST INSTRUMENTAL VARIABLE REGRESSION (2012, background): “See Van der Laan and Robins (2003) for a textbook account and Kang, Joseph, and Schafer (2007), Lunceford and Davidian (2004), and Neugebauer and Van der Laan (2005) for comparative reviews.”