{"id":200477,"date":"2024-11-30T07:43:21","date_gmt":"2024-11-30T13:43:21","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/11\/a-modern-approach-to-the-fundamental-problem-of-causal-inference"},"modified":"2024-11-30T07:43:21","modified_gmt":"2024-11-30T13:43:21","slug":"a-modern-approach-to-the-fundamental-problem-of-causal-inference","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/11\/a-modern-approach-to-the-fundamental-problem-of-causal-inference","title":{"rendered":"A Modern Approach To The Fundamental Problem of Causal Inference"},"content":{"rendered":"

<\/a><\/p>\n

Originally published on Towards AI.<\/p>\n

ABSTRACT: The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation<\/a>, in other words: correlation<\/a> does not prove causality. This problem can be understood from two points of view: experimental and statistical. The experimental approach tells us that this problem arises from the impossibility of simultaneously observing an event both in the presence and absence of a hypothesis. The statistical approach, on the other hand, suggests that this problem stems from the error of treating tested hypotheses as independent of each other. Modern statistics<\/a> tends to place greater emphasis on the statistical approach because, compared to the experimental point of view, it also shows us a way to solve the problem. Indeed, when testing many hypotheses, a composite hypothesis is constructed that tends to cover the entire solution space. Consequently, the composite hypothesis can be fitted to any data set by generating a random correlation. Furthermore, the probability that the correlation is random is equal to the probability of obtaining the same result by generating an equivalent number of random hypotheses.<\/p>\n","protected":false},"excerpt":{"rendered":"

Originally published on Towards AI. ABSTRACT: The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation, in other words: correlation does not prove causality. This problem can be understood from two points of view: experimental and statistical. The experimental approach tells us that this problem arises from the [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,8],"tags":[],"class_list":["post-200477","post","type-post","status-publish","format-standard","hentry","category-robotics-ai","category-space"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/200477","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=200477"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/200477\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=200477"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=200477"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=200477"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}