[wahern]

https://en.wikipedia.org/wiki/Abductive_reasoning

> [Abductive reasoning] starts with an observation or set of observations and then seeks the simplest and most likely conclusion from the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely". One can understand abductive reasoning as inference to the best explanation.

> In the 1990s, as computing power grew, the fields of law, computer science, and artificial intelligence research spurred renewed interest in the subject of abduction.

Abductive reasoning is basically how one would formally describe 1) the practice of medicine, including diagnosis, 2) the rules for evidence in legal trials, 3) the process for generating hypotheses in science, and innumerable similar activities we undertake daily.

And for obvious reasons there's a close relationship between abductive reasoning and Bayesian statistics.

[bmc7505]

There are some good papers which take inspiration from abductive reasoning to study automatic knowledge base construction and structured inference here in case anyone is interested: https://www.cs.utexas.edu/~ml/publications/area/65/abduction

[yobbo]

If you want to talk about bayes, you could think about causation as a prior for correlation. We can measure correlation (variates), and then we can infer something about the causation (independent variables).

"Inference to the best explanation" could mean we accept any explanation regardless of how improbable it is - as long as it best explains the data.

The bayesian idea is that we can learn something about causation if we accept uncertainty and impose "sanity constraints" (priors) on the explanation.

Without knowing the real-world mechanics of Y, we can say something like "setting X to 0.33 will increase Y, with 60% probability." It maybe impossible to learn anything else from the data.

[ahmedelsama]

I like this a lot. I studied Bayesian mathematic and in my opinion it’s the beat approach to solving these problems. Start with a prior and continue to update your state with measurements. This avoids a lot of the common pitfalls when doing batch ML and getting junk results