Causality scientist Judea Pearl looks at causality and some of the limitations of machine learning systems. Technical paper, but useful scan for practitioners.
The seven tools of causal inference with reflections on machine learning
The seven tools of causal inference with reflections on machine learning Pearl, CACM 2018
With thanks to @osmandros for sending me a link to this paper on twitter.
In this technical report Judea Pearl reflects on some of the limitations of machine learning systems that are based solely on statistical interpretation of data. To understand why? and to answer what if? questions, we need some kind of a causal model. In the social sciences and especially epidemiology, a transformative mathematical framework called ‘Structural Causal Models’ (SCM) has seen widespread adoption. Pearl presents seven example tasks which the model can handle, but which are out of reach for associational machine learning systems. ... "
Subscribe to:
Post Comments (Atom)
No comments:
Post a Comment