Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect.

By T. Kurth, A. Walker, Robert J. Glynn, K. Chan, J. M. Gaziano, K. Berger, James M. Robins. American Journal of Epidemiology, 2006

755 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/07eca08d9ff09e79dfeb369f19571fb8b87c52e0

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Citations in the corpus

Assessing the Effectiveness of a College Freshman Seminar Using Propensity Score Adjustments (2011, background): “While some researchers have found no relationship between depression and either GPA or retention (DeBerard et al. 2004); others have found that depression moderates the relationship between students’ perceptions of themselves as students and GPA (Lane and Gibbons 2007) and those who are depressed ar”
Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores (2018, methodology): “…studies based on estimated propensity scores was first used in medical applications (e.g., Vincent et al., 2002; Grzybowski et al., 2003; Kurth et al., 2005) and then formalized by Crump et al. (2009), who suggested dropping units from the analysis which have estimated propensity…”
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative (2011, methodology): “We implement an extension of standard inverse probability of treatment weighting that has been discussed in several other articles (see, e.g., Imbens, 2004; Kurth et al., 2006) that is appropriate for the treatment on the treated estimand.”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “The reader is referred elsewhere to empirical studies comparing the results of analyses using the different propensity score methods on the same data set (Austin & Mamdani, 2006; Kurth et al., 2006).”
Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness (2015, background): “The catch is that matching will usually alter the distribution of B in the total cohort, thus altering the distribution of Y1 - Y0 and hence its average D if (as one should expect) B includes modifiers of the differences Y1 - Y0 [32].”
Hospital and societal costs of antimicrobial-resistant infections in a Chicago teaching hospital: implications for antibiotic stewardship. (2009, methodology): “Finally, to address the sampling bias, propensity scores were used to select matched control subjects for each patient with ARI in a case-control study [30–32].”
Matching methods for causal inference: A review and a look forward (2010, background): “See Imbens (2004), Kurth et al. (2006) and Imai, King and Stuart (2008) for further discussion of these distinctions.”
An introduction to propensity-score methods for reducing confounding in observational studies (2011, neutral): “The reader is referred elsewhere to empirical studies comparing the results of analyses using the different propensity score methods on the same data set (Austin & Mamdani, 2006; Kurth et al., 2006).”
Bayesian Nonparametric Modeling for Causal Inference (2011, background): “The second propensity-score-based estimator is a straightforward extension (Imbens 2004; Kurth et al. 2006) of standard inverse-probability-of-treatment-weighted (IPTW) estimators (Rosenbaum 1987; Robins 1999).”
Improving propensity score weighting using machine learning (2010, methodology): “A number of propensity score weighting schemes have been applied in the literature [3, 13, 29, 30].”
Assessing lack of common support in causal inference using bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes (2013, methodology): “The focus in those comparisons is on methods that are reasonably simple to understand and implement: standard linear regression, propensity score matching (with regression adjustment), and inverse probability of treatment weighted linear regression [IPTW; Imbens (2004), Kurth et al. (2006)].”
The prognostic analogue of the propensity score (2008, result): “…propensity scores, the hypothesis has been corroborated in a variety of studies (see e.g. Drake 1993; Dehejia and Wahba 1999; Rubin and Thomas 2000; Kurth et al. 2006; Rubin and Stuart 2006); however, propensity balance can be checked for a whole sample, whereas prognostic balance can ordinarily…”
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research (2010, methodology): “One applied study uses a single estimated PS paired with multiple PS application methods and reports all resulting treatment effect estimates (Kurth et al., 2006), and although they do report a large variability in results, they do not present criteria for comparing or selecting between their…”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, background): “Thus, controls are reweighted to look like treated observations when estimating the ATT, or vice versa when estimating the ATC (Imbens, 2004; Kurth et al., 2006).”