Abstract
Ceiling lines were recently proposed to identify necessary conditions as constraints on the outcome in a scatterplot. However, these lines do not work very well on large data sets with random observation error. This paper suggests an alternate way of empirically assessing probabilistic necessary conditions in large and noisy data sets using sigmoidal activation functions, which describe the propensity of outcome at different levels of the independent variable.
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