Blog Feuilleton · leon.bottou.org
https://leon.bottou.org/
2019-12-09T10:49:17-0500leon.bottou.org
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https://leon.bottou.org/lib/images/favicon.icotext/html2018-09-10T10:18:23-0500Léon BottouConfounding
https://leon.bottou.org/feuilleton/confounding-simpson#
Confounding
This post presents and comments a classic example of confounding variable in real life.
The intricacies of the phenomenon and their relation to Bayes'rule
are discussed in a second post).
Kidney stones
Kidney stones are hard mineral deposits that form inside the kidneys,
can lodge themselves in annoying locations of the urinary tract,
become very painful, and may have to be removed. A 1986 studytext/html2018-08-29T17:46:41-0500Léon BottouProbabilistic vs. Causal inference
https://leon.bottou.org/feuilleton/confounding-bayes#
<- Confounding
Probabilistic vs. Causal inference
This post revisits the confounding phenomenon under the
angle of probabilistic reasoning with conditional probabilities
and Bayes' theorem. This analysis reveals what
sets probabilistic and causal inference apart. It also
shows how probabiistic models and causal models
formulate assumptions of a fundamentally different nature.$Y$$X$$Z$$Z$$Y$$(Z,X)$$P(Z,X,Y)$$P(Y|Z,X)$$Z$$X$$P(Y|Z,X)$$(Z,X)$$Z$\begin{equation}
\label{eq:xyz}
P(Z,X,Y) =…text/html2018-08-27T15:25:14-0500Léon BottouCausation without decisions
https://leon.bottou.org/feuilleton/causation_and_decisions#
Causation without decisions
The foundational texts on statistical causationGuido W. Imbens, Donald B. Rubin, 2015. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press. frequently remind us that causal inference is necessary to estimate the outcomes of decisions. Suppose for instance that we have access to a trove of medical reports describing the outcomes $Z$$Y$$X$$P(Z|X,Y)$text/html2018-05-24T11:41:10-0500Léon BottouAI for the open world
https://leon.bottou.org/feuilleton/turing#
AI for the open world
We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of…