Answers.Body: <p>An example may clarify.</p>
  <p>When more sunscreen is purchased, more swimsuits are purchased. This does not mean, however, that the sunscreen seeps into our skin, makes its way to our brain, and tells us to buy swimsuits. There is another underlying factor at play: it is summer, so people are going to the beach and the pool a lot and need both sunscreen and new swimsuits. That is, there is a mere correlation between sunscreen sales and swimsuits sales, nothing causal.</p>
  <p>Nonetheless, if you, for whatever reason, did not have access to a calendar or a thermometer, if you wanted to know when your clothing store should have a large inventory of swimsuits, an uptick in sunscreen sales might be a reliable predictor of the demand for new swimsuits. That is, despite the lack of a causal mechanism between the sunscreen and the swimsuits, you might be able to have a reasonably accurate predictive model (a simple form of AI/ML) using sunscreen to make your predictions about swimsuits.</p>
  <p>Note, however, that if it became popular to wear sunscreen all year and not just in the summer (this was recommended at my last physical), such a predictor about swimsuits would fail to make good predictions about swimsuit demand when summer hits, since sunscreen is not the true causal mechanism. Nonetheless, the mere correlation may be a reliable predictor in many cases and may be the best you can expect to get in a complicated situation.</p>
  
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Answers.Body: <p>Most predictive machine learning, that creates model with input and output, is completely agnostic to <em>how</em> or <em>why</em> two the two sets of variables (inputs and outputs) are linked.</p>
  <p>As such the model learning process will use any observed correlation between inputs and outputs observed to inform the learning process and reduce loss.</p>
  <p>This doesn't prevent the process creating an approximate causal model. However, unless you put the effort in to uncover causation and structure the learning process to model it, then you cannot tell.</p>
  <p>This problem is not limited to machine learning. It's a common issue in scientific studies of all kinds, and you will often hear the warning &quot;correlation doesn't imply causation&quot;, when e.g. studies show people who eat a particular diet have a certain incidence of an interesting health outcome. It is rarely strong proof that the diet causes the health issue, at least not on its own.</p>
  <p>To answer your question more directly, then yes most uses of ML build models of correlation. However, this is not really a model class issue. You could structure experiments and data collection to isolate causation using many of the same model types.</p>
  
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Body: <p>From <a href="https://en.wikipedia.org/wiki/Causal_AI" rel="nofollow noreferrer">Wikipedia</a></p>
  <blockquote>
  <p>Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision.</p>
  </blockquote>
  <p>Are the AI like LR, SVM or ANN correlated? If yes, how come?</p>
  
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Comments.Text: The statement does not say that "AI is correlated" but that "they make inferences using correlation". So, maybe this question should be reformulated. Also, can you please provide the link to where we can find the quoted text?
Comments.UserDisplayName: nbro
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Comments.Text: I've tried to save this question by making some assumptions about what you really want to ask and about the source of the quote, but please next time do try to write a clearer question and provide the link to the article that you're quoting.
Comments.UserDisplayName: nbro
Tags: <machine-learning><comparison><correlation><causation>
Title: What do we mean by "AI is correlated"?
UserDisplayName: quanity
ThreadText: # What do we mean by "AI is correlated"?
  
  (Asked by: username_0 on 2024-05-24)
  
  From [Wikipedia](https://en.wikipedia.org/wiki/Causal_AI)
  
  > Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision.
  
  Are the AI like LR, SVM or ANN correlated? If yes, how come?
  
  **Tags:** machine-learning, comparison, correlation, causation
  
  > **Comments:**
  > **username_1**: The statement does not say that "AI is correlated" but that "they make inferences using correlation". So, maybe this question should be reformulated. Also, can you please provide the link to where we can find the quoted text?  
  > **username_1**: I've tried to save this question by making some assumptions about what you really want to ask and about the source of the quote, but please next time do try to write a clearer question and provide the link to the article that you're quoting.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2024-10-22. Score: 0
  
  An example may clarify.
  
  When more sunscreen is purchased, more swimsuits are purchased. This does not mean, however, that the sunscreen seeps into our skin, makes its way to our brain, and tells us to buy swimsuits. There is another underlying factor at play: it is summer, so people are going to the beach and the pool a lot and need both sunscreen and new swimsuits. That is, there is a mere correlation between sunscreen sales and swimsuits sales, nothing causal.
  
  Nonetheless, if you, for whatever reason, did not have access to a calendar or a thermometer, if you wanted to know when your clothing store should have a large inventory of swimsuits, an uptick in sunscreen sales might be a reliable predictor of the demand for new swimsuits. That is, despite the lack of a causal mechanism between the sunscreen and the swimsuits, you might be able to have a reasonably accurate predictive model (a simple form of AI/ML) using sunscreen to make your predictions about swimsuits.
  
  Note, however, that if it became popular to wear sunscreen all year and not just in the summer (this was recommended at my last physical), such a predictor about swimsuits would fail to make good predictions about swimsuit demand when summer hits, since sunscreen is not the true causal mechanism. Nonetheless, the mere correlation may be a reliable predictor in many cases and may be the best you can expect to get in a complicated situation.
  
  
  ---
  
  ### Answer by username_3 on 2024-05-24. Score: 0
  
  Most predictive machine learning, that creates model with input and output, is completely agnostic to *how* or *why* two the two sets of variables (inputs and outputs) are linked.
  
  As such the model learning process will use any observed correlation between inputs and outputs observed to inform the learning process and reduce loss.
  
  This doesn't prevent the process creating an approximate causal model. However, unless you put the effort in to uncover causation and structure the learning process to model it, then you cannot tell.
  
  This problem is not limited to machine learning. It's a common issue in scientific studies of all kinds, and you will often hear the warning "correlation doesn't imply causation", when e.g. studies show people who eat a particular diet have a certain incidence of an interesting health outcome. It is rarely strong proof that the diet causes the health issue, at least not on its own.
  
  To answer your question more directly, then yes most uses of ML build models of correlation. However, this is not really a model class issue. You could structure experiments and data collection to isolate causation using many of the same model types.
  
  
  ---
  
  