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We attempt to clarify, and suggest how to avoid, several serious
misunderstandings about and fallacies of causal inference in
experimental and observational research. These issues concern some
of the most basic advantages and disadvantages of each basic
research design. Problems include improper use of hypothesis tests
for covariate balance between the treated and control groups, and
the consequences of using randomization, blocking before
randomization, and matching after treatment assignment to achieve
covariate balance. Applied researchers in a wide range of
scientific disciplines seem to fall prey to one or more of these
fallacies, and as a result make suboptimal design or analysis
choices. To clarify these points, we derive a new four-part
decomposition of the key estimation errors in making causal
inferences. We then show how this decomposition can help scholars
from different experimental and observational research traditions
better understand each other's inferential problems and attempted
solutions.
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In July 2009, this paper was recognized by Thomson Reuters' ScienceWatch as New Hot Paper, for the most-cited paper in the field of
Economics & Business in the last two months among papers
published in the last year.
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You may also be interested in Ho, Daniel,
Kosuke Imai, Gary King, and Elizabeth A. Stuart. (2007). ``Matching as Nonparametric
Preprocessing for Improving Parametric Causal
Inference.'' Political Analysis, Vol. 15,
No.3 (Summer), pp. 199-236.
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