<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>bertil</author><text>The main reason for not using causal inference is not because data scientists don’t know about the different approaches or can’t imagine something equivalent (a lot of reinvention); forecasting is one of the most common tasks, after all.&lt;p&gt;The main reason is that they generally work for software companies where it’s easier and less susceptible to analyst influence to implement the suggested change and test it with a Random Control Trial. I remember running an analysis that found that gender was a significant explaining factor for behavior on our site; my boss asked (dismissively): What can we do with that information? If there is an assumption of how things work that doesn’t translate to a product change, that insight isn’t useful; if there is a product intuition, testing the product change itself is key, and there’s no reason to delay that.&lt;p&gt;There are cases where RCTs are hard to organize (for example, multi-sided platform businesses) of changes that can’t be tested in isolation (major brand changes). Those tend to benefit from the techniques described there——and they have dedicated teams. But this is a classic case of a complicated tool that doesn’t fit most use cases.</text><parent_chain></parent_chain></comment><story><title>Causal inference as a blind spot of data scientists</title><url>https://dzidas.com/ml/2023/10/15/blind-spot-ds/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>Anon84</author><text>For a hands on introduction to Causality, I would recommend
“Causal Inference in Python” by M. Facure &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;amzn.to&amp;#x2F;46byWnl&quot; rel=&quot;nofollow noreferrer&quot;&gt;https:&amp;#x2F;&amp;#x2F;amzn.to&amp;#x2F;46byWnl&lt;/a&gt;
Well written and to the point.&lt;p&gt;&amp;lt;ShamelessSelfPromotion&amp;gt;
I also have a series of blog posts on the topic: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;DataForScience&amp;#x2F;Causality&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;DataForScience&amp;#x2F;Causality&lt;/a&gt; where I work through Pearls Primer: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;amzn.to&amp;#x2F;3gsFlkO&quot; rel=&quot;nofollow noreferrer&quot;&gt;https:&amp;#x2F;&amp;#x2F;amzn.to&amp;#x2F;3gsFlkO&lt;/a&gt;
&amp;lt;&amp;#x2F;ShamelessSelfPromotion&amp;gt;</text><parent_chain></parent_chain></comment><story><title>Causal inference as a blind spot of data scientists</title><url>https://dzidas.com/ml/2023/10/15/blind-spot-ds/</url></story>