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**James M. Robins and Larry Wasserman**

However, we shall show that SGS's asymptotics implicitly assume that
probability of there being ``no unmeasured common causes'' of **X** and **Y**\
is positive and not small relative to sample size. We prove that, under an
asymptotics for which the probability of ``no unmeasured common causes'' is
small relative to sample size, causal relationships are non-identifiable
from the data alone, even when we assume distributions are faithful to the
causal graph. We argue that, in observational epidemiologic, econometric,
and social scientific studies, a formal asymptotic analysis that models the
probability of ``no unmeasured common causes'' as small relative to sample
size accurately reflects the beliefs of practicing professionals. We argue
that these beliefs derive both from experience and from the fact that the
world contains so many potential unmeasured common causes (i.e.,
confounders) that it is * a priori* highly unlikely that not a single one
actually causes both **X** and **Y**. We conclude that, in observational
studies, small causal effects can never be either reliably ruled in or ruled
out; furthermore, one should not make the leap from even relatively large
empirical associations to causation without substantive
subject-matter-specific background information.

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