scholarly journals Automatic variable selection for exposure‐driven propensity score matching with unmeasured confounders

2020 ◽  
Vol 62 (3) ◽  
pp. 868-884
Author(s):  
Daniela Zöller ◽  
Leesa F. Wockner ◽  
Harald Binder
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Propensity Score Matching ◽  
Selection For ◽  
Unmeasured Confounders

scholarly journals Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference

2016 ◽  
Vol 12 (1) ◽  
pp. 97-115 ◽  
Author(s):  
Mireille E. Schnitzer ◽  
Judith J. Lok ◽  
Susan Gruber
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Causal Inference ◽  
Simulation Study ◽  
Learning Approaches ◽  
Minimum Loss ◽  
Knowledge Based ◽  
Flexible Modeling ◽  
Selection For ◽  
Highly Correlated

Abstract This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios.

scholarly journals Variable Selection for Propensity Score Models

2006 ◽  
Vol 163 (12) ◽  
pp. 1149-1156 ◽  
Author(s):  
M. Alan Brookhart ◽  
Sebastian Schneeweiss ◽  
Kenneth J. Rothman ◽  
Robert J. Glynn ◽  
Jerry Avorn ◽  
...  
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Selection For

scholarly journals RE: "VARIABLE SELECTION FOR PROPENSITY SCORE MODELS"

2007 ◽  
Vol 166 (2) ◽  
pp. 238-239 ◽  
Author(s):  
I. Shrier ◽  
R. W. Platt ◽  
R. J. Steele
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Selection For

scholarly journals Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

2012 ◽  
Vol 22 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Richard Wyss ◽  
Cynthia J. Girman ◽  
Robert J. LoCasale ◽  
M. Alan Brookhart ◽  
Til Stürmer
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Simulation Study ◽  
Treatment Effects ◽  
Multiple Outcomes ◽  
Selection For

Variable Selection for Propensity Score Estimation via Balancing Covariates

Epidemiology ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. e14-e15 ◽  
Author(s):  
Yeying Zhu ◽  
Maya Schonbach ◽  
Donna L. Coffman ◽  
Jennifer S. Williams
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Selection For

scholarly journals The role of the c -statistic in variable selection for propensity score models

2010 ◽  
Vol 20 (3) ◽  
pp. 317-320 ◽  
Author(s):  
Daniel Westreich ◽  
Stephen R. Cole ◽  
Michele Jonsson Funk ◽  
M. Alan Brookhart ◽  
Til Stürmer
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
C Statistic ◽  
Selection For

Besondere Förderung von Kern- kompetenzen an Spezialgymnasien?

2010 ◽  
Vol 24 (1) ◽  
pp. 5-22 ◽  
Author(s):  
Jürgen Baumert ◽  
Michael Becker ◽  
Marko Neumann ◽  
Roumiana Nikolova
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching

Der vorliegende Beitrag geht der Frage nach, ob Schülerinnen und Schüler, die nach der vierten Klasse in Berlin in ein grundständiges Gymnasium wechseln, in Abhängigkeit vom Profil des besuchten Gymnasiums im Vergleich zu Grundschülern mit vergleichbaren Lernvoraussetzungen unterschiedliche Lernzuwächse im Leseverständnis, in Mathematik und Englisch erreichen. Auf der Datengrundlage der ELEMENT-Studie wurde die Leistungsentwicklung von Schülerinnen und Schülern an grundständigen Gymnasien (N = 1758) und Grundschulen (N = 3169) während der 5. und 6. Jahrgangsstufe mithilfe von Propensity Score Matching-Analysen (PSM) modelliert. Nach Kontrolle von leistungsrelevanten Unterschieden zwischen den Schülergruppen am Ende der 4. Jahrgangsstufe zeigten sich für das Leseverständnis am Ende der 6. Klasse keine statistisch signifikanten Unterschiede. Für die Mathematikleistung ließen sich Unterschiede lediglich zugunsten eines grundständigen Gymnasiums, das zum Untersuchungszeitpunkt noch kein spezifisches Profil entwickelt hatte, nachweisen. In der Domäne Englisch, in der die curricularen Unterschiede zwischen den Schulzweigen stärker akzentuiert sind, wurden positive Ergebnisse im Vergleich zu den Grundschulen für die so genannten Schnellläuferzüge, die englisch-bilingualen Klassen und das grundständige Gymnasium ohne spezifisches Profil ermittelt. Die Lernstände am Ende der 6. Klasse in den altsprachlichen Gymnasien fielen dagegen im Vergleich zu den Grundschulen geringer aus. Die Befunde widersprechen der Annahme, dass mit dem frühen Übergang auf ein grundständiges Gymnasium automatisch eine besondere Förderung der Lesefähigkeit und des mathematischen Verständnisses besonders leistungsfähiger Schülerinnen und Schüler erreicht werde. Die Ergebnisse zu den Englischleistungen weisen hingegen darauf hin, dass Unterschiede in der Leistungsentwicklung auftreten können, sofern die Aufteilung auf Schulen mit unterschiedlichen Bildungsprogrammen mit curricularen Unterschieden im Unterricht einhergeht. Methodische und inhaltliche Implikationen der Befunde und Grenzen ihrer Generalisierbarkeit werden diskutiert.

Validating Four Standard Scales in Spiritually Practicing and Nonpracticing Samples Using Propensity Score Matching

2008 ◽  
Vol 24 (3) ◽  
pp. 165-173 ◽  
Author(s):  
Niko Kohls ◽  
Harald Walach
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Repeated Measures ◽  
Propensity Scores ◽  
Total Sample ◽  
Experimental Manipulation ◽  
Brief Symptom Inventory ◽  
Time Interaction ◽  
Social Support Scale ◽  
Lower Test

Validation studies of standard scales in the particular sample that one is studying are essential for accurate conclusions. We investigated the differences in answering patterns of the Brief-Symptom-Inventory (BSI), Transpersonal Trust Scale (TPV), Sense of Coherence Questionnaire (SOC), and a Social Support Scale (F-SoZu) for a matched sample of spiritually practicing (SP) and nonpracticing (NSP) individuals at two measurement points (t1, t2). Applying a sample matching procedure based on propensity scores, we selected two sociodemographically balanced subsamples of N = 120 out of a total sample of N = 431. Employing repeated measures ANOVAs, we found an intersample difference in means only for TPV and an intrasample difference for F-SoZu. Additionally, a group × time interaction effect was found for TPV. While Cronbach’s α was acceptable and comparable for both samples, a significantly lower test-rest-reliability for the BSI was found in the SP sample (rSP = .62; rNSP = .78). Thus, when researching the effects of spiritual practice, one should not only look at differences in means but also consider time stability. We recommend propensity score matching as an alternative for randomization in variables that defy experimental manipulation such as spirituality.

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