<story><title>Causality in machine learning</title><url>http://www.unofficialgoogledatascience.com/2017/01/causality-in-machine-learning.html</url></story><parent_chain></parent_chain><comment><author>nkurz</author><text>I worked on a project a few years ago for a UC Berkeley biostatistics professor who believes he has a theoretically proven approach of using non-randomized observational data to create an asymptotically-efficient unbiased estimator of counterfactual treatments.  My impression as a programmer who has only dabbled in machine learning is that this is a phenomenal claim far beyond the state of the art.&lt;p&gt;My job was to take an inefficient proof-of-concept R package, and make it computationally and memory efficient enough to run on real-world datasets.   I failed totally.   The obvious explanation would be that I just wasn&amp;#x27;t able to understand the math involved well enough to implement the algorithm despite immersing myself in it for months. My personal guess though is that both the paper and the reference implementation were flawed in some way that made the task impossible.&lt;p&gt;Anyway, the paper is here:&lt;p&gt;Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models&lt;p&gt;Petersen, Schwab, Gruber, Blaser, Schomacher, and van der Laan; J Causal Inference 2015&lt;p&gt;&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.ncbi.nlm.nih.gov&amp;#x2F;pmc&amp;#x2F;articles&amp;#x2F;PMC4405134&amp;#x2F;pdf&amp;#x2F;nihms675179.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.ncbi.nlm.nih.gov&amp;#x2F;pmc&amp;#x2F;articles&amp;#x2F;PMC4405134&amp;#x2F;pdf&amp;#x2F;nih...&lt;/a&gt;&lt;p&gt;And the R package here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;joshuaschwab&amp;#x2F;ltmle&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;joshuaschwab&amp;#x2F;ltmle&lt;/a&gt;.&lt;p&gt;My understanding was never perfect, but my belief is that there is kernel of insight in this approach that has not yet been explored in machine learning.   Alternatively, maybe it works as is, and just needs a better implementation.   I&amp;#x27;d love to see someone implement this approach, or fix it, or discredit it.  As it is, I think it&amp;#x27;s potentially incredibly valuable work that is getting very little attention.</text></comment>
<story><title>Causality in machine learning</title><url>http://www.unofficialgoogledatascience.com/2017/01/causality-in-machine-learning.html</url></story><parent_chain></parent_chain><comment><author>nonbel</author><text>Causality has never been of much interest to me. I never understood why others make such a big deal out of it. Maybe it is a real thing, maybe illusion, definitely some kind of heuristic... but shouldn&amp;#x27;t we be searching for useful &amp;quot;laws&amp;quot; (eg F ~ m1*m2&amp;#x2F;r^2) rather than &amp;quot;cause&amp;quot;? I know some have denied causality a place in science:&lt;p&gt;&amp;quot;In the following paper I wish, first, to maintain that the word &amp;quot;cause&amp;quot; is so inextricably bound up with misleading associations as to make its complete extrusion from the philosophical vocabulary desirable; secondly, to inquire what principle, if any, is employed in science in place of the supposed &amp;quot;law of causality&amp;quot; which philosophers imagine to be employed; thirdly, to exhibit certain confusions, especially in regard to teleology and determinism, which appear to me to be connected with erroneous notions as to causality.&amp;quot;
&lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.hist-analytic.com&amp;#x2F;Russellcause.pdf&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.hist-analytic.com&amp;#x2F;Russellcause.pdf&lt;/a&gt;</text></comment>