(Page 9 of 18) - Tentative Answers to Questions about Causal Mechanisms authored by Mahoney, James.

knowledge integrators and the proposition derivers is that the former scholars start with
pre-existing and already tested propositions and then work to a set of postulates from
which these propositions can be logically deduced, whereas the latter researchers begin
with the postulates and work forward to propositions that were not previously tested or
known.

Knowledge integration helps overcome the fragmentation that arises when

researchers discover a diverse range of independent variables that are all statistically
associated with a particular outcome. This strategy can show how the associations are
the product of a single causal mechanism, supplementing empirical research that begins
without the aid of a general theory. However, when compared to proposition deriving,
the exercise of knowledge integration provides less convincing support for the existence
of a causal mechanism, given that the validity of the propositions was already known in
advance (e.g., Abell 1994; Cohen 1989; Hage 1994).

Finally, outcome explanation refers to the theoretical practice of logically

deducing particular historical outcomes or events – rather than testable hypotheses – from
a set of postulates. A historical outcome might be anything from occurrence of the
French Revolution to the electoral victory of George W. Bush. When using this strategy,
the analyst does not test the outcome that makes up the final proposition, since this
occurrence already has taken place. Rather, the analyst seeks to test as many as possible
of the postulates used to logically deduce the outcome. The strategy of outcome
explaining is thus fundamentally different than the strategies discussed above – in
particular, outcome explaining violates the maxim “test the propositions, never the
postulates.” Scholars using this strategy test the postulates because they contain the
hypothesized explanation for the outcome. If these analysts cannot establish empirical
support for the postulates, they are in a weak position arguing that the postulates explain
the outcome at hand. In general, therefore, outcome explainers seek to formulate
postulates that are highly testable and falsifiable but empirically supported.

Because causal mechanisms are unobserved entities, the initial postulate about the

causal mechanism cannot be tested in the outcome-explaining strategy. It is therefore
imperative that outcome explainers formulate other postulates that can be empirically
evaluated. If these other postulates are eventually supported, one’s confidence in the
existence of the causal mechanism increases, provided that the overall set of postulates
allows for the logical derivation of the outcome of interest. In the end, nevertheless, the
analyst must make a leap of faith in the existence of the causal mechanism, since this
beginning postulate is never directly tested, and since the final proposition (i.e., the
outcome of interest) is also not tested (because it has already taken place).

In important respects, the strategy of outcome explaining is best suited for case

study and small-N researchers, who often seek to explain particular outcomes. By
contrast, knowledge integration is more useful to statistical researchers, who often
discover that many heterogeneous variables are related to an outcome, but lack a means

An exception would be an outcome that has not occurred but is predicted to occur in the future.

Successful predictions of this kind provide strong support for a general theory. However, historical
sociologists study outcomes that occurred in the past, making this possibility less relevant to the discussion
at hand.