  Inducing causal relationships from observations is a classic problem in
machine learning. Most work in causality starts from the premise that the
causal variables themselves are observed. However, for AI agents such as robots
trying to make sense of their environment, the only observables are low-level
variables like pixels in images. To generalize well, an agent must induce
high-level variables, particularly those which are causal or are affected by
causal variables. A central goal for AI and causality is thus the joint
discovery of abstract representations and causal structure. However, we note
that existing environments for studying causal induction are poorly suited for
this objective because they have complicated task-specific causal graphs which
are impossible to manipulate parametrically (e.g., number of nodes, sparsity,
causal chain length, etc.). In this work, our goal is to facilitate research in
learning representations of high-level variables as well as causal structures
among them. In order to systematically probe the ability of methods to identify
these variables and structures, we design a suite of benchmarking RL
environments. We evaluate various representation learning algorithms from the
literature and find that explicitly incorporating structure and modularity in
models can help causal induction in model-based reinforcement learning.
