  Causal induction, i.e., identifying unobservable mechanisms that lead to the
observable relations among variables, has played a pivotal role in modern
scientific discovery, especially in scenarios with only sparse and limited
data. Humans, even young toddlers, can induce causal relationships surprisingly
well in various settings despite its notorious difficulty. However, in contrast
to the commonplace trait of human cognition is the lack of a diagnostic
benchmark to measure causal induction for modern Artificial Intelligence (AI)
systems. Therefore, in this work, we introduce the Abstract Causal REasoning
(ACRE) dataset for systematic evaluation of current vision systems in causal
induction. Motivated by the stream of research on causal discovery in Blicket
experiments, we query a visual reasoning system with the following four types
of questions in either an independent scenario or an interventional scenario:
direct, indirect, screening-off, and backward-blocking, intentionally going
beyond the simple strategy of inducing causal relationships by covariation. By
analyzing visual reasoning architectures on this testbed, we notice that pure
neural models tend towards an associative strategy under their chance-level
performance, whereas neuro-symbolic combinations struggle in backward-blocking
reasoning. These deficiencies call for future research in models with a more
comprehensive capability of causal induction.
