Comparing Different Propensity Score Estimation Methods for Estimating the Marginal Causal Effect Through Standardization to Propensity Scores Publisher



Gharibzadeh S¹ ; Mansournia MA¹ ; Rahimiforoushani A¹ ; Alizadeh A¹ ; Amouzegar A² ; Mehrabanizeinabad K³ ; Mohammad K¹ Show Affiliations
Source: Communications in Statistics: Simulation and Computation Published:2018

Abstract

Hernan and Robins proposed a method for calculating marginal causal effect of treatment using standardization to propensity scores.  Data adaptive methods have been suggested as alternatives to logistic regression for the estimation of propensity scores. We examined the performance of various data mining methods using simulated data. The estimators' performance was evaluated in terms of relative bias, 95% CI coverage rate, and mean squared error.  All methods (except CART and GBM) displayed generally acceptable performance. However, under the conditions of moderate non-additivity and moderate nonlinearity, ANN and SL outperformed logistic regression with better bias reduction and more consistent 95% CI coverage. © 2018, © 2018 Taylor & Francis Group, LLC.