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Causal reasoning has been an indispensable capability for humans and otherintelligent animals to interact with the physical world. In this work, wepropose to endow an artificial agent with the capability of causal reasoningfor completing goal-directed tasks. We develop learning-based approaches toinducing causal knowledge in the form of directed acyclic graphs, which can beused to contextualize a learned goal-conditional policy to perform tasks innovel environments with latent causal structures. We leverage attentionmechanisms in our causal induction model and goal-conditional policy, enablingus to incrementally generate the causal graph from the agent's visualobservations and to selectively use the induced graph for determining actions.Our experiments show that our method effectively generalizes towards completingnew tasks in novel environments with previously unseen causal structures.

Causal Induction from Visual Observations for Goal Directed Tasks