Towards Understanding How Machines Can Learn Causal Overhypotheses

Eliza Kosoy, David Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Huang, Jessica B Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik

Keywords: causal reasoning, intervention, causal overhypotheses, Reinforcement learning, gpt-3

TL;DR: We present a new flexible environment which allows for the evaluation of existing techniques under variable causal overhypotheses

Abstract: Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn causal overhypotheses, and use these to guide their exploration or to generalize to new situations. This has been demonstrated in a variety of cognitive science experiments using the “blicket detector” environment. We present a causal learning benchmark adapting the “blicket" environment for machine learning agents and evaluate a range of state-of-the-art methods in this environment. We find that although most agents have no problem learning causal structures seen during training, they are unable to learn causal overhypotheses from these experiences, and thus cannot generalize to new settings.

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