<story><title>The Slippery Math of Causation</title><url>https://www.quantamagazine.org/the-math-of-causation-puzzle-20180530/</url></story><parent_chain></parent_chain><comment><author>cschmidt</author><text>Since the OP is prompted by Judea Pearl&amp;#x27;s new book, I&amp;#x27;ll ask here.  There seem to be at least two schools of thought in causal statistics.  The first is championed by Judea Pearl [1,2,3] and the other by Donald Rubin [4].&lt;p&gt;If I want to learn causal statistics, for use in ML, which school of thought would be more useful? I don&amp;#x27;t mean to prompt any causal flame wars, but it isn&amp;#x27;t obvious which approach is more useful.&lt;p&gt;&lt;pre&gt;&lt;code&gt;    [1] https:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Book-Why-Science-Cause-Effect&amp;#x2F;dp&amp;#x2F;046509760X
    [2] https:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Causality-Reasoning-Inference-Judea-Pearl&amp;#x2F;dp&amp;#x2F;052189560X
    [3] https:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Causal-Inference-Statistics-Judea-Pearl&amp;#x2F;dp&amp;#x2F;1119186846

    [4] https:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Causal-Inference-Statistics-Biomedical-Sciences&amp;#x2F;dp&amp;#x2F;0521885884&lt;/code&gt;&lt;/pre&gt;</text></comment>
<story><title>The Slippery Math of Causation</title><url>https://www.quantamagazine.org/the-math-of-causation-puzzle-20180530/</url></story><parent_chain></parent_chain><comment><author>macawfish</author><text>Just wanted to throw out a related Quanta article about Sugihara&amp;#x27;s method: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.quantamagazine.org&amp;#x2F;chaos-theory-in-ecology-predicts-future-populations-20151013&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.quantamagazine.org&amp;#x2F;chaos-theory-in-ecology-predi...&lt;/a&gt;&lt;p&gt;Sugihara&amp;#x27;s method is a way of quantifying causal relationships in nonlinear systems with attractors that would be cumbersome to model (hence the &amp;quot;equation free&amp;quot; description).</text></comment>