<story><title>Causal Inference in Statistics: A Primer</title><url>http://bayes.cs.ucla.edu/PRIMER/</url></story><parent_chain><item><author>ced</author><text>I was looking for a review, and found Gelman&amp;#x27;s recommendation of&lt;p&gt;&lt;i&gt;Causal Inference for Statistics, Social, and Biomedical Sciences&lt;/i&gt;&lt;p&gt;and mention of this book: &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.hsph.harvard.edu&amp;#x2F;miguel-hernan&amp;#x2F;causal-inference-book&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.hsph.harvard.edu&amp;#x2F;miguel-hernan&amp;#x2F;causal-inference-b...&lt;/a&gt;&lt;p&gt;Does anyone have a comment on those? I&amp;#x27;ve read Pearl&amp;#x27;s two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it&amp;#x27;s hard to connect the more advanced theorems with anything I could actually implement.</text></item></parent_chain><comment><author>xtacy</author><text>I too found Pearl&amp;#x27;s book hard to navigate on first attempt.  Do not let that stop you!  After a hiatus, I stumbled upon this blog post [1], which explained the core ideas in Pearl&amp;#x27;s framework beautifully in a simple language.  My advice is to persist, fill any holes in fundamentals (mostly basic probability), and persist.  After working out the examples in the blog post on paper and contrasting it to other ideas out there (potential outcome framework), it became quite clear what Pearl was trying to articulate.&lt;p&gt;Pearl is also an enthusiastic speaker.  You can search for his talks online at various venues (Stanford, Microsoft Research, etc.) to learn more.&lt;p&gt;[1] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.michaelnielsen.org&amp;#x2F;ddi&amp;#x2F;if-correlation-doesnt-imply-causation-then-what-does&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.michaelnielsen.org&amp;#x2F;ddi&amp;#x2F;if-correlation-doesnt-impl...&lt;/a&gt;</text></comment>
<story><title>Causal Inference in Statistics: A Primer</title><url>http://bayes.cs.ucla.edu/PRIMER/</url></story><parent_chain><item><author>ced</author><text>I was looking for a review, and found Gelman&amp;#x27;s recommendation of&lt;p&gt;&lt;i&gt;Causal Inference for Statistics, Social, and Biomedical Sciences&lt;/i&gt;&lt;p&gt;and mention of this book: &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.hsph.harvard.edu&amp;#x2F;miguel-hernan&amp;#x2F;causal-inference-book&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.hsph.harvard.edu&amp;#x2F;miguel-hernan&amp;#x2F;causal-inference-b...&lt;/a&gt;&lt;p&gt;Does anyone have a comment on those? I&amp;#x27;ve read Pearl&amp;#x27;s two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it&amp;#x27;s hard to connect the more advanced theorems with anything I could actually implement.</text></item></parent_chain><comment><author>hudibras</author><text>Gelman&amp;#x27;s own book (with Jennifer Hill) also has some good, practical techniques on causal inference. [0]&lt;p&gt;Morgan and Winship&amp;#x27;s &lt;i&gt;Counterfactuals and Causal Inference: Methods and Principles for Social Research&lt;/i&gt; [1] is also really good. Be sure to get the second edition; it&amp;#x27;s much better than the first.&lt;p&gt;[0] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Analysis-Regression-Multilevel-Hierarchical-Models&amp;#x2F;dp&amp;#x2F;052168689X&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Analysis-Regression-Multilevel-Hierarc...&lt;/a&gt;&lt;p&gt;[1] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Counterfactuals-Causal-Inference-Principles-Analytical&amp;#x2F;dp&amp;#x2F;1107694167&amp;#x2F;ref=dp_ob_title_bk&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.amazon.com&amp;#x2F;Counterfactuals-Causal-Inference-Princ...&lt;/a&gt;</text></comment>