On the Interpretation of do(x)do(x)

Pearl Judea ()
Additional contact information
Pearl Judea: Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA90024, USA

Journal of Causal Inference, 2019, vol. 7, issue 1, 6

Abstract: This paper provides empirical interpretation of the do(x)do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x)do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines to learn effects of untried actions from those tried. We end with the conclusion that researchers need not distinguish manipulable from non-manipulable variables; both types are equally eligible to receive the do(x)do(x) operator and to produce useful information for decision makers.

Keywords: Manipulability; causal effects; interventions; testability; reinforcement learning (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2019-2002 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:7:y:2019:i:1:p:6:n:3

DOI: 10.1515/jci-2019-2002

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().