BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models
Benjamin Brown, Kai Zhang, Xiao-Li Meng

TL;DR
This paper introduces the BELIEF framework, which leverages atomic linearity in binary data to reinterpret generalized linear models as transparent linear models, offering new insights and solutions to issues like complete separation.
Contribution
The paper develops the BELIEF framework that reinterprets GLMs as linear models, providing interpretability and addressing estimation issues such as complete separation.
Findings
BELIEF offers a linear perspective on binary relationships.
It reveals that zero interaction coefficients in GLMs do not imply no interaction.
BELIEF automatically detects perfect predictors in complete separation cases.
Abstract
Two linearly uncorrelated binary variables must be also independent because non-linear dependence cannot manifest with only two possible states. This inherent linearity is the atom of dependency constituting any complex form of relationship. Inspired by this observation, we develop a framework called binary expansion linear effect (BELIEF) for understanding arbitrary relationships with a binary outcome. Models from the BELIEF framework are easily interpretable because they describe the association of binary variables in the language of linear models, yielding convenient theoretical insight and striking Gaussian parallels. With BELIEF, one may study generalized linear models (GLM) through transparent linear models, providing insight into how the choice of link affects modeling. For example, setting a GLM interaction coefficient to zero does not necessarily lead to the kind of…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Neural Networks and Applications
MethodsGLM
