{"id":1738,"date":"2016-09-11T18:08:50","date_gmt":"2016-09-11T18:08:50","guid":{"rendered":"http:\/\/causality.cs.ucla.edu\/blog\/?p=1738"},"modified":"2016-09-13T01:15:55","modified_gmt":"2016-09-13T01:15:55","slug":"an-interesting-math-and-causality-minded-club","status":"publish","type":"post","link":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/2016\/09\/11\/an-interesting-math-and-causality-minded-club\/","title":{"rendered":"An interesting math and causality-minded club"},"content":{"rendered":"<p>from Adam Kelleher:<\/p>\n<p>The math and algorithm reading group (<a href=\"http:\/\/www.meetup.com\/Math-and-Algorithm-Reading-Group\/\">http:\/\/www.meetup.com\/Math-and-Algorithm-Reading-Group\/<\/a>) is based in NYC, and was founded when I moved here three years ago. It&#8217;s a very casual group that grew out of a reading group I was in during graduate school. Some friends who were math graduate students were interested in learning more about general relativity, and I (a physicist) was interested in learning more math. Together, we read about differential geometry, with the goal of bringing our knowledge together. We reasoned that we could learn more as a group, by pooling our different perspectives and experience, than we could individually. That&#8217;s the core motivation of our reading group: not only are we there to help resolve each other get through the material if anyone gets stuck, but we&#8217;re also there to add what else we know (in the format of a group discussion) to the content of the material.<\/p>\n<p>We&#8217;re currently reading <em>Causality<\/em> cover to cover. We&#8217;ve paused to implement some of the algorithms, and plan on pausing again soon for a review session. We intend to do a &#8220;hacking session&#8221;, to try our hands at causal inference and analysis on some open data sets.<\/p>\n<p>Inspired by reading <em>Causality<\/em>, and realizing that the best open implementations of causal inference were packaged in the (old, relatively inaccessible) Tetrad package, I&#8217;ve started a modern implementation of some tools for causal inference and analysis in the causality package in Python. It&#8217;s on pypi (pip install causality, or check the tutorial on <a href=\"http:\/\/www.github.com\/akelleh\/causality\">http:\/\/www.github.com\/akelleh\/causality<\/a>), but it&#8217;s still a work in progress. The IC* algorithm is implemented, along with a small suite of conditional independence tests. I&#8217;m adding some classic methods for causal inference and causal effects estimation, aimed at making the package more general-purpose. I invite new contributions to help build out the package. Just open an issue, and label it an &#8220;enhancement&#8221; to kick of the discussion!<\/p>\n<p>Finally, to make all of the work more accessible to people without more advanced math background, I&#8217;ve been writing a series of blog posts aimed at introducing anyone with an intermediate background in probability and statistics to the material in Causality! It&#8217;s aimed especially at practitioners, like data scientists. The hope is that more people, managers included (the intended audience for the first 3 posts), will understand the issues that come up when you&#8217;re not thinking causally. I&#8217;d especially recommend the article about understanding bias <a href=\"https:\/\/medium.com\/@akelleh\/understanding-bias-a-pre-requisite-for-trustworthy-results-ee590b75b1be#.qw7n8qx8d\">https:\/\/medium.com\/@akelleh\/understanding-bias-a-pre-requisite-for-trustworthy-results-ee590b75b1be#.qw7n8qx8d<\/a>, but the whole series (still in progress) is indexed here: <a href=\"https:\/\/medium.com\/@akelleh\/causal-data-science-721ed63a4027#.v7bqse9jh\">https:\/\/medium.com\/@akelleh\/causal-data-science-721ed63a4027#.v7bqse9jh<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>from Adam Kelleher: The math and algorithm reading group (http:\/\/www.meetup.com\/Math-and-Algorithm-Reading-Group\/) is based in NYC, and was founded when I moved here three years ago. It&#8217;s a very casual group that grew out of a reading group I was in during graduate school. Some friends who were math graduate students were interested in learning more about [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-1738","post","type-post","status-publish","format-standard","hentry","category-announcement"],"_links":{"self":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1738","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1738"}],"version-history":[{"count":4,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1738\/revisions"}],"predecessor-version":[{"id":1757,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1738\/revisions\/1757"}],"wp:attachment":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1738"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1738"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1738"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}