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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>David Heckerman</author_name><author_url>https://www.microsoft.com/en-us/research/people/heckerma/</author_url><title>A Decision-Based View of Causality - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="WTWMD7pch5"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/decision-based-view-causality/"&gt;A Decision-Based View of Causality&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/decision-based-view-causality/embed/#?secret=WTWMD7pch5" width="600" height="338" title="&#x201C;A Decision-Based View of Causality&#x201D; &#x2014; Microsoft Research" data-secret="WTWMD7pch5" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able to predict the effects of actions. In this paper, we attempt to unite two branches of research that address such predictions: causal [&hellip;]</description></oembed>
