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Evaluating the Use of Ridge Regression and Principal Components in Propensity Score Estimators under Multicollinearity
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Multicollinearity can be present in the propensity score model when estimating average treatment effects (ATEs). In this thesis, logistic ridge regression (LRR) and principal components logistic regression (PCLR) are evaluated as an alternative to ML estimation of the propensity score model. ATE estimators based on weighting (IPW), matching and stratification are assessed in a Monte Carlo simulation study to evaluate LRR and PCLR. Further, an empirical example of using LRR and PCLR on real data under multicollinearity is provided. Results from the simulation study reveal that under multicollinearity and in small samples, the use of LRR reduces bias in the matching estimator, compared to ML. In large samples PCLR yields lowest bias, and typically was found to have the lowest MSE in all estimators. PCLR matched ML in bias under IPW estimation and in some cases had lower bias. The stratification estimator was heavily biased compared to matching and IPW but both bias and MSE improved as PCLR was applied, and for some cases under LRR. The specification with PCLR in the empirical example was usually most sensitive as a strongly correlated covariate was included in the propensity score model.

Place, publisher, year, edition, pages
2014. , 54 p.
Keyword [en]
Causal Inference, Propensity Score, IPW estimator, Stratification, Matching, Logistic Ridge Regression, Principal Components Logistic Regression
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-226924OAI: diva2:727738
Subject / course
Educational program
Master Programme in Statistics
Available from: 2014-06-24 Created: 2014-06-23 Last updated: 2014-06-24Bibliographically approved

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