A Decision-Based View of Causality

David Heckerman, Ross Schachter
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:259-270, 1995.

Abstract

We present a precise definition of cause and effect in terms of a more fundamental notion called unresponsiveness. Our definition departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not necessarily attaching a causal explanation to every dependency. Such local reasoning can be beneficial in that, given a set of real decisions to make, it may not be necessary to determine whether some dependencies are causal. Also in this paper, we examine the graphical encoding of causal relationships. We show that ordinary influence diagrams are an inadequate representation of cause, whereas influence diagrams in Howard Canonical Form can always represent cause and effect accurately. In addition, we establish a correspondence between Pearl and Verma’s (1991) causal model and the influence diagram.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-heckerman95a, title = {A Decision-Based View of Causality}, author = {Heckerman, David and Schachter, Ross}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {259--270}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/heckerman95a/heckerman95a.pdf}, url = {https://proceedings.mlr.press/r0/heckerman95a.html}, abstract = {We present a precise definition of cause and effect in terms of a more fundamental notion called unresponsiveness. Our definition departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not necessarily attaching a causal explanation to every dependency. Such local reasoning can be beneficial in that, given a set of real decisions to make, it may not be necessary to determine whether some dependencies are causal. Also in this paper, we examine the graphical encoding of causal relationships. We show that ordinary influence diagrams are an inadequate representation of cause, whereas influence diagrams in Howard Canonical Form can always represent cause and effect accurately. In addition, we establish a correspondence between Pearl and Verma’s (1991) causal model and the influence diagram.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T A Decision-Based View of Causality %A David Heckerman %A Ross Schachter %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-heckerman95a %I PMLR %P 259--270 %U https://proceedings.mlr.press/r0/heckerman95a.html %V R0 %X We present a precise definition of cause and effect in terms of a more fundamental notion called unresponsiveness. Our definition departs from the traditional view of causation in that our causal assertions are made relative to a set of decisions. An important consequence of this departure is that we can reason about cause locally, not necessarily attaching a causal explanation to every dependency. Such local reasoning can be beneficial in that, given a set of real decisions to make, it may not be necessary to determine whether some dependencies are causal. Also in this paper, we examine the graphical encoding of causal relationships. We show that ordinary influence diagrams are an inadequate representation of cause, whereas influence diagrams in Howard Canonical Form can always represent cause and effect accurately. In addition, we establish a correspondence between Pearl and Verma’s (1991) causal model and the influence diagram. %Z Reissued by PMLR on 01 May 2022.
APA
Heckerman, D. & Schachter, R.. (1995). A Decision-Based View of Causality. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:259-270 Available from https://proceedings.mlr.press/r0/heckerman95a.html. Reissued by PMLR on 01 May 2022.

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