Predictive Coding beyond Correlations
CoRR(2023)
Abstract
Recently, there has been extensive research on the capabilities of
biologically plausible algorithms. In this work, we show how one of such
algorithms, called predictive coding, is able to perform causal inference
tasks. First, we show how a simple change in the inference process of
predictive coding enables to compute interventions without the need to mutilate
or redefine a causal graph. Then, we explore applications in cases where the
graph is unknown, and has to be inferred from observational data. Empirically,
we show how such findings can be used to improve the performance of predictive
coding in image classification tasks, and conclude that such models are able to
perform simple end-to-end causal inference tasks.
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Key words
causal inference
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