What do We Gain? Combining Propensity Score Methods and Multilevel Modeling

32 Pages Posted: 13 Aug 2009 Last revised: 1 Oct 2009

See all articles by Yu-Sung Su

Yu-Sung Su

Tsinghua University

Jeronimo Cortina

University of Houston - Department of Political Science

Date Written: 2009

Abstract

The fundamental problem of causal inference is that an individual cannot be simultaneously observed in both the treatment and control states (Holland 1986). Propensity score methods that compare the treatment and control groups by discarding the unmatched units are now widely used to deal with this problem. Propensity score matching works well when using individual level data (persons, countries, counties, etc.); however, when using data that have a multilevel structure, such as time-series-cross-sectional (TSCS) data we need to combine propensity score matching procedures with multilevel modeling in order to take into account the unique structure of the data. In this paper we conduct Monte Carlo simulations with 36 different scenarios to test the performance of the two combined methods. The result shows that combining propensity score methods with multilevel modeling yields less biased and more efficient estimates. Two empirical case studies that reexamine the relationship between democratization and development and democracy and militarized interstate disputes also show the advantage of combining these two methods.

Keywords: causal inference, balancing score, multilevel modeling, propensity score, time-series cross-sectional data

Suggested Citation

Su, Yu-Sung and Cortina, Jeronimo, What do We Gain? Combining Propensity Score Methods and Multilevel Modeling (2009). APSA 2009 Toronto Meeting Paper. Available at SSRN: https://ssrn.com/abstract=1450058

Yu-Sung Su (Contact Author)

Tsinghua University ( email )

153 MIngzhai, Qinghua Yuan
Haidian District
Beijing, Beijing 100084
China
+86 13810661799 (Phone)

Jeronimo Cortina

University of Houston - Department of Political Science ( email )

TX 77204-3011
United States

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