Factor Selection in Observational Studies -- An Application of Nonlinear Factor Selection to Propensity Scores

Observational studies have become a major research methodology in scienti c disciplines where experiments are hard to perform. These most notably include the health sciences, social sciences and (macro-)economics. It is difficult to estimate treatment e ects due to the non-randomised character of these studies. Propensity scores solve this problem to some extent by incorporating the control variables into one measure [13]. An estimation of these propensity scores can be performed by any method of categorical regression (logit etc.). Nevertheless, estimated eff ects are very sensitive to the propensity score [7, 8, 1] and for this reason, the propensity score should be estimated with great care. In order to avoid a poor generalisation performance of the propensity score estimation, it is important to choose factors appropriately. Unfortunately, it is not possible to select factors during the estimation of the propensity score and independently from treatment calculation. In this paper, an integrated factor selection method is proposed, which considers the treatment e ffect estimation during the propensity score estimation.

Dlugosz, Stephan (2010), Factor Selection in Observational Studies -- An Application of Nonlinear Factor Selection to Propensity Scores, in: Hermann Locarek-Junge, Claus Weihs (Eds.), Studies in Classification, Data Analysis, and Knowledge Organization, Springer-Verlag, Berlin-Heidelberg-New York, 361-369