What constitutes a causal explanation, and must an explanation be
causal? What warrants a causal inference, as opposed to a
descriptive regularity? What techniques are available to detect
when causal effects are present, and when can these techniques be
used to identify the relative importance of these effects? What
complications do the interactions of individuals create for these
techniques? When can mixed methods of analysis be used to deepen
causal accounts? Must causal claims include generative mechanisms,
and how effective are empirical methods designed to discover them?
The Handbook of Causal Analysis for Social Research tackles these
questions with nineteen chapters from leading scholars in
sociology, statistics, public health, computer science, and human
development.
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