 The material above has largely focused on the use of interventionist or manipulability based ideas to provide an interpretation of causal claims, with little attention paid to the use of these ideas in causal inference—that is, inference to causal relationships from experimental and non-experimental data. The latter is an important subject in its own right. Roughly speaking, if one thinks of causal claims as claims about the outcomes of possible manipulations or experiments, then this suggests distinctive ways of conceptualizing problems of causal inference from non-experimental data: these may be conceptualized as problems of inferring from such data (and other assumptions) what the outcome of a possible experiment would be without doing the experiment in question. This point of view can used to motivate or rationalize the use of such procedures as instrumental variables or regression discontinuity designs—see, e.g., Angrist and Pischke 2009 for econometric applications of these ideas. 