A simple algorithm to avoid misspecifying the propensity score equation

Abstract

"Propensity score matching is a semi-parametric method to balance covariates to estimate the causal effect of a treatment, intervention, or action on a specific outcome. The propensity score is typically estimated with a logistic regression or similar parametric model for binary outcomes. Therefore, model specification still plays an important role even if the causal effect is estimated nonparametrically in the matched sample. Methodological research indicates that misspecifying the propensity score equation leads to biased estimates. We build on Dehejia and Wahba (2002) and propose a re-specification algorithm. The algorithm is shown to reduce bias in a Monte Carlo simulation, especially in small samples. Notably, the algorithm reduces bias regardless of whether the propensity score equation is misspecified or correctly specified. We present guidelines for using the algorithm in applied research based on an empirical analysis with real data." (Author's abstract, IAB-Doku) ((en))

Cite article

Krug, G. (2014): A simple algorithm to avoid misspecifying the propensity score equation. Evidence from a Monte Carlo simulation. (LASER discussion papers 83), Erlangen, 5 p.