TOWARDS UNDERSTANDING HOW MACHINES CAN LEARN 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.

1. INTRODUCTION

Over the past few years, deep learning has made impressive progress in many areas, including reinforcement learning, natural language processing, and computer vision. However, most stateof-the-art algorithms still pale in comparison to humans, even children, for out-of-distribution generalization and fast adaptation to new tasks. In contrast, causal modeling has long concerned itself not merely with accurate modeling of in-distribution data, but also the accurate recovery of underlying causal mechanisms (and their true graphical relations) capable of explaining out-of-distribution data. Hence causal modeling holds promise for achieving systematic generalization (Bengio et al., 2019; Schölkopf et al., 2021; Ke et al., 2021) . One of the key components of causal learning for humans are causal overhypotheses, which describe priors over causal graphs. For example, a causal hypothesis may be that a system is stochastic, or that it is conjuntive-i.e., more than one cause must be present in order for the effect to occur. Causal overhypotheses are important because they enable humans to learn causal models from a sparse amount of data (Griffiths & Tenenbaum, 2009) , by reducing the set of possible causal relationships to consider. Despite the recent surge of machine learning datasets and environment for causal inference and learning (Ahmed et al., 2020; McDuff et al., 2021; Wang et al., 2021a; Ke et al., 2021) , the causal overhypotheses for these environments and datasets are unclear. Thus we cannot use these existing benchmarks to evaluate how capable are agents are at learning and using causal overhypotheses. In this work, we seek to fill this gap, by focusing on introducing a benchmark environment that draws inspiration from recent cognitive science work using blicket detectors. This environment contains a virtual blicket detector, and has been used previously for understanding causal learning and exploration in children (Kosoy et al., 2022) and basic RL models, but not yet for state-of-the-art machine learning agents. A "blicket detector" is a machine that lights up and plays music when some combinations of objects but not others are placed on it (Gopnik & Sobel, 2000; Lucas et al., 2014) . The central question is whether an agent can learn that a particular set of causal events will lead to the lighting-up effect, and use that knowledge to design novel interventions on the machine. The causal relationship is entirely determined by the pattern of conditional dependencies and interventions, rather than requiring intuitive physics knowledge or visual understanding. Although these tasks may seem simple, and are easily mastered by children, we find that they are challenging for current learning algorithms.

