In view of persistent ambiguities regarding the definition of "causal inference" (CI) I am sharing here the definition that has guided me successfully throughout my journeys. CI is a method that takes data from various sources, as well as extra-data information, and produces
answers to questions of two types (1) the effects of pending interventions and (2) the effects of hypothetical undoing of past events. See Causality (2000) Chapter 1. A vivid and recurrent example of a non-causal question is any question that can be answered from the joint
probability distribution of observed variables, eg, correlation, partial
regression, Granger causality, weak and strong endogeneity
(EHR 1983) etc. See ucla.in/2N9f28c .
This definition excludes Pearson's (1911) and Fisher's
(1925) descriptions of statistical tasks
and I would reserve judgment on how "experimental economists" fit into this definition. I believe that, in due time, "experimental economists" will manage to articulate formally what "extra-data information" they use, and thus become bonified members of CI. #Bookofwhy