Estimation of Causal Effect and Adjustment of Survey Data using Propensity Scores

Abstract

In behavioral sciences, it is often difficult to execute an experimental study with random assignment. Therefore researchers usually do a quasi-experiment or a survey study without random assignment.
However, under these studies the distributions of the covariates that would affect dependent variables usually differ with the values of the independent variables.
To eliminate the influence of the covariates, various adjustment methods such as analysis of covariance have been applied to these data.
Recently new adjustment methods using the propensity score proposed by Rosenbaum & Rubin (1983) have been applied to many researches especially in the areas of medicine or economics, and these methods also attract attention in behavioral sciences.
The propensity score methods are also used for adjustment of survey data.
In this paper, we give a detailed explanation of several estimation methods of causal effect using the propensity scores and related topics.
We also review adjustment methods of biased survey data using the propensity scores.