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.
The three layer causal hierarchy
A useful insight unveiled by the theory of causal models is the classification of causal information in terms of the kind of questions that each class is capable of answering. This classification forms a 3-level hierarchy in the sense that questions at level i (i = 1, 2 ,3 ) can only be answered if information from level j (j ≥ i) is available.

The lowest (first) layer is called Association and it involves purely statistical relationships defined by the naked data. This Continue reading




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