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| public:t-720-atai:atai-22:causation [2022/10/03 14:22] – [Explanation & Explainability] thorisson | public:t-720-atai:atai-22:causation [2025/04/27 11:39] (current) – [Probability] thorisson |
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| | \\ Why It Is Important \\ in AI | Probability enters into our knowledge of anything for which the knowledge is //**incomplete**//. \\ As in, //everything that humans do every day in every real-world environment//. \\ With incomplete knowledge it is in principle //impossible to know what may happen//. However, if we have very good models for some //limited// (small, simple) phenomenon, we can expect our prediction of what may happen to be pretty good, or at least //**practically useful**//. This is especially true for knowledge acquired through the scientific method, in which empirical evidence and human reason is systematically brought to bear on the validity of the models. | | | \\ Why It Is Important \\ in AI | Probability enters into our knowledge of anything for which the knowledge is //**incomplete**//. \\ As in, //everything that humans do every day in every real-world environment//. \\ With incomplete knowledge it is in principle //impossible to know what may happen//. However, if we have very good models for some //limited// (small, simple) phenomenon, we can expect our prediction of what may happen to be pretty good, or at least //**practically useful**//. This is especially true for knowledge acquired through the scientific method, in which empirical evidence and human reason is systematically brought to bear on the validity of the models. | |
| | How To Compute Probabilities | Most common method is Bayesian networks, which encode the concept of probability in which probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief [[https://en.wikipedia.org/wiki/Bayesian_probability|REF]]. Which makes it useful for representing an (intelligent) agent's knowledge of some environment, task or phenomenon. | | | How To Compute Probabilities | Most common method is Bayesian networks, which encode the concept of probability in which probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief [[https://en.wikipedia.org/wiki/Bayesian_probability|REF]]. Which makes it useful for representing an (intelligent) agent's knowledge of some environment, task or phenomenon. | |
| | \\ How It Works | \\ <m>P(a|b)={P(b|a)~P(a)}/{P(b)}</m> | | | \\ How It Works | \\ P(a#b)={P(b#a)~P(a)}/{P(b)} \\ where # means 'given'. | |
| | Judea Pearl | Most Fervent Advocate (and self-proclaimed inventor) of Bayesian Networks in AI [[http://ftp.cs.ucla.edu/pub/stat_ser/R246.pdf|REF]]. | | | Judea Pearl | Most Fervent Advocate (and self-proclaimed inventor) of Bayesian Networks in AI [[http://ftp.cs.ucla.edu/pub/stat_ser/R246.pdf|REF]]. | |
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| | \\ More Recent History | Causation has been cast by the wayside in statistics for the past 120 years, saying instead that all we can claim about the relationship of any variables is that they correlate. Needless to say this has lead to significant confusion as to what science can and cannot say about causal relationships, such as whether mobile phones cause cancer. Equally badly, the statistical stance has infected some scientific fields to view causation as "unscientific". | | | \\ More Recent History | Causation has been cast by the wayside in statistics for the past 120 years, saying instead that all we can claim about the relationship of any variables is that they correlate. Needless to say this has lead to significant confusion as to what science can and cannot say about causal relationships, such as whether mobile phones cause cancer. Equally badly, the statistical stance has infected some scientific fields to view causation as "unscientific". | |
| | Spurious Correlation | Non-zero correlation due to complete coincidence. | | | Spurious Correlation | Non-zero correlation due to complete coincidence. | |
| | \\ Causation & Correlation | What is the relation between causation and correlation? \\ There is no (non-spurious) correlation without causation. \\ There is no causation without correlation. \\ However, causation between two variables does necessitate one of them to be the cause of the other: They can have a shared (possibly hidden) //common cause//. | | | \\ Causation & Correlation | What is the relation between causation and correlation? \\ There is no (non-spurious) correlation without causation. \\ There is no causation without correlation. \\ However, correlation between two variables does not necessitate one of them to be the cause of the other: They can have a shared (possibly hidden) //common cause//. | |
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