Bayesian and causal inference are fundamental processes for intelligence.
Bayesian inference models observations: what can be inferred about y if we
observe a related variable x? Causal inference models interventions: if we
directly change x, how will y change? Predictive coding is a
neuroscience-inspired method for performing Bayesian inference on continuous
state variables using local information only. In this work, we go beyond
Bayesian inference, and show how a simple change in the inference process of
predictive coding enables interventional and counterfactual inference in
scenarios where the causal graph is known. We then extend our results, and show
how predictive coding can be generalized to cases where this graph is unknown,
and has to be inferred from data, hence performing causal discovery. What
results is a novel and straightforward technique that allows us to perform
end-to-end causal inference on predictive-coding-based structural causal
models, and demonstrate its utility for potential applications in machine
learning.

本文介绍了一种新的技术，它利用预测编码在结构性因果模型上进行端到端因果推断，并在机器学习中展示其实用性，且通过将预测编码推广到无法从数据推断因果关系图的情况下实现因果发现。