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Causal Inference of Ambiguous Manipulations Peter Spirtes*
 

Summary: Causal Inference of Ambiguous Manipulations
by
Peter Spirtes*
and Richard Scheines*
1. Introduction
Among other things, causal hypotheses ought to predict how the world will respond to an
intervention. How much will we reduce our risk of stroke by switching to a low-fat diet?
How will the chances of another terrorist attack change if the U.S. invades Iraq next
week? Causal inference is the move from data and background knowledge to justified
causal hypotheses. Epistemologically, we want to characterize the conditions under
which we can do causal inference, that is, what sorts of data and background knowledge
can be converted into knowledge of how the world will respond to an intervention. Over
the last two decades, philosophers, statisticians, and computer scientists have converged
substantially on at least the fundamental outline of a theory of causation that provides a
precise theory of causal knowledge and causal inference (Spirtes, Glymour, and Scheines,
2000; Pearl, 2000). Different researchers give slightly different accounts of the idea of a
manipulation, or an intervention, but all assume that when we intervene ideally to directly
set the value of exactly one variable, it matters not how we set it in predicting how the
rest of the system will respond. This assumption turns out to be problematic, primarily
because it often does matter how one sets the value of a variable one is manipulating. In

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics