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Caveats for Causal Reasoning with Equilibrium Models

Dash, Denver (2003) Caveats for Causal Reasoning with Equilibrium Models. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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This thesis raises objections to the use of causal reasoning with equilibrium models. I consider two operators that are used to transform models: the {em Do} operator for modeling manipulation and the {em Equilibration} operator for modeling a system that has achieved equilibrium. I introduce a property of a causal model called the {em EMC Property} that is true iff the {em Do} operator commutes with the {em Equilibration} operator. I prove that not all models obey the EMC property, and I demonstrate empirically that when inferring a causal model from data, the learned model will not support causal reasoning if the EMC property is not obeyed. I find sufficient conditions for models to violate and not to violate the EMC property. In addition, I show that there exists a class of models that violate EMC and possess a set of variables whose manipulation will cause an instability in the system. All dynamic models in this class possess feedback, although I do not prove that feedback is a necessary or a sufficient condition for EMC violation. I define the {em Structural Stability Principle} which provides a necessary graphical criterion for stability in causal models. I will argue that the models in this class are quite common given typical assumptions about causal relations.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee Chairzdzel, Marekmarek@sis.pitt.eduDRUZDZEL
Committee MemberCooper,
Committee MemberHauskrecht, Milosmilos@cs.pitt.eduMILOS
Committee MemberScheines,
Committee MemberGopalakrishnan,
Date: 23 May 2003
Date Type: Completion
Defense Date: 18 March 2003
Approval Date: 23 May 2003
Submission Date: 7 May 2003
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian networks; causality; Do operator; dynamics; feedback; structural equation models
Other ID:, etd-05072003-102145
Date Deposited: 10 Nov 2011 19:43
Last Modified: 15 Nov 2016 13:43


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