Title:      Causal Inference of Repeated Observations: A
Synthesis of the Propensity Score Methods and Multilevel
Modeling

Authors:    Yu-Sung Su

Entrydate:  2008-07-03 19:52:36

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

Abstract:   The fundamental problem of causal inference is that
an individual cannot be simultaneously observed in both the
treatment and control states. The propensity score matching
methods that compare the treatment and control groups by
discarding the unmatched units is now widely used to deal with
this problem. In some situations, however, it is possible to
observe the same individual or unit of observation in the
treatment and control states at different points in time. The
data has the structure that is often refer to as time-series
cross sectional (TSCS) data. While multilevel modeling is often
applied to analyze TSCS data, synthesizing the propensity score
methods and multilevel modeling is preferable.

The paper conduct a Monte Carlo simulation with 36 different
scenarios to test the performance of the two combined methods. 
The result shows that synthesizing the propensity score matching
with multilevel modeling is preferable in that such method yield
less biased and more efficient estimates. An empirical case
study that reexamine the model of Przeworksi et al (2000) on
democratization and development also shows the advantage of this
synthesis.

http://polmeth.wustl.edu/retrieve.php?id=772

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