{"id":1910,"date":"2019-01-09T06:59:06","date_gmt":"2019-01-09T06:59:06","guid":{"rendered":"http:\/\/causality.cs.ucla.edu\/blog\/?p=1910"},"modified":"2019-01-09T06:59:06","modified_gmt":"2019-01-09T06:59:06","slug":"can-causal-inference-be-done-in-statistical-vocabulary","status":"publish","type":"post","link":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/2019\/01\/09\/can-causal-inference-be-done-in-statistical-vocabulary\/","title":{"rendered":"Can causal inference be done in statistical vocabulary?"},"content":{"rendered":"\n

Andrew Gelman has just posted a review of The Book of Why (https:\/\/andrewgelman.com\/2019\/01\/08\/book-pearl-mackenzie\/<\/a>),\u00a0my answer to some of his comments follows below:<\/p>\n\n\n\n

“Andrew,\u00a0<\/p>\n\n\n\n

The hardest thing for people to snap out of is the bubble of their own language. You say:\u00a0“I find it baffling that Pearl and his colleagues keep taking\u00a0statistical problems and, to my mind, complicating them by wrapping them in a causal structure (see, for example, here).”\u00a0<\/p>\n\n\n\n

No way! and again: No way! There is no way to answer causal questions without snapping out of statistical vocabulary.\u00a0 I have tried to demonstrate it to you in the past several years, but was not able to get you to solve ONE toy problem\u00a0from beginning to end.\u00a0<\/p>\n\n\n\n

This will remain a perennial stumbling block until one of your\u00a0readers tries honestly to solve ONE toy problem from beginning to end.\u00a0No links to books or articles, no naming of fancy statistical\u00a0techniques, no global economics problems,\u00a0just a simple causal question whose answer we know in advance. (e.g. take Simpson’s paradox: Which data should be consulted?\u00a0The aggregated or the disaggregated?)\u00a0<\/p>\n\n\n\n

Even this group of 73 Editors found it impossible, and have\u00a0issued the following guidelines for reporting observational studies: https:\/\/www.atsjournals.org\/doi\/pdf\/10.1513\/AnnalsATS.201808-564PS<\/a><\/p>\n\n\n\n

To readers of your blog: Please try it. The late Dennis Lindley\u00a0was the only statistician I met who had the courage to admit:\u00a0 “We need to enrich our language with a do-operator”. Try it,\u00a0and you will see why he came to this conclusion, and perhaps\u00a0you will also see why Andrew is unable to follow him.”<\/p>\n\n\n\n

Addendum:<\/em><\/strong><\/p>\n\n\n\n

In his response to my comment above, Andrew Gelman\u00a0suggested that we agree to disagree, since science is full of disagreements and there is lots of room for progress using different methods. Unfortunately, the need to enrich statistics with new vocabulary is a mathematical fact, not an opinion. This need cannot be resolved by “there are many ways to skin a cat” without snapping out of traditional statistical language and enriching it\u00a0 with causal vocabulary.\u00a0 Neyman-Rubin’s potential outcomes vocabulary is an example of such enrichment, since it goes beyond joint distributions of observed variables.<\/p>\n\n\n\n

Andrew further refers us to three chapters in his book (with Jennifer Hill) on causal inference. I am craving instead for one toy problem, solved from assumptions to conclusions, so that we can follow precisely the roll played by the extra-statistical vocabulary, and why it is absolutely needed. The Book of Why presents dozen such\u00a0examples, but readers would do well to choose their own.<\/p>\n","protected":false},"excerpt":{"rendered":"

Andrew Gelman has just posted a review of The Book of Why (https:\/\/andrewgelman.com\/2019\/01\/08\/book-pearl-mackenzie\/),\u00a0my answer to some of his comments follows below: “Andrew,\u00a0 The hardest thing for people to snap out of is the bubble of their own language. You say:\u00a0“I find it baffling that Pearl and his colleagues keep taking\u00a0statistical problems and, to my mind, […]<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1910","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1910","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\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1910"}],"version-history":[{"count":3,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1910\/revisions"}],"predecessor-version":[{"id":1913,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1910\/revisions\/1913"}],"wp:attachment":[{"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1910"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1910"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/causality.cs.ucla.edu\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1910"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}