Distinguishing cause from effect based on exogeneity


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Recent developments in structural equation modeling have produced several methods that can usually distinguish cause from effect in the two-variable case. For that purpose, how- ever, one has to impose substantial structural constraints or smoothness assumptions on the functional causal mod- els. In this paper, we consider the problem of determining the causal direction from a related but different point of view, and propose a new framework for causal direction de- termination. We show that it is possible to perform causal inference based on the condition that the cause is “exoge- nous” for the parameters involved in the generating pro- cess from the cause to the effect. In this way, we avoid the structural constraints required by the SEM-based ap- proaches. In particular, we exploit nonparametric methods to estimate marginal and conditional distributions, and pro- pose a bootstrap-based approach to test for the exogeneity condition; the testing results indicate the causal direction between two variables. The proposed method is validated on both synthetic and real data.
Original languageEnglish
Title of host publicationProceedings of the 15th conference on Theoretical Aspects of Rationality and Knowledge
PublisherTARK 2015
Number of pages11
ISBN (Print)9780692461389
Publication statusPublished - 22 Apr 2015
EventFifteenth conference on Theoretical Aspects of Rationality and Knowledge - Carnegie Mellon University, Pittsburgh, United States
Duration: 4 Jun 20156 Jun 2015


ConferenceFifteenth conference on Theoretical Aspects of Rationality and Knowledge
Abbreviated titleTARK 2015
Country/TerritoryUnited States
OtherTARK 2015 took place at the Carnegie Mellon University, Pittsburgh, USA during June 4--6, 2015.
The mission of the TARK conferences is to bring together researchers from a wide variety of fields, including Artificial Intelligence, Cryptography, Distributed Computing, Economics and Game Theory, Linguistics, Philosophy, and Psychology, in order to further our understanding of interdisciplinary issues involving reasoning about rationality and knowledge.
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  • Causal discovery
  • causal direction
  • exogeneity
  • statistical independence
  • bootstrap


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