Causal Reasoning from Meta-reinforcement learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data. Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions. Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches. More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.
 This submission is an great ablation study on the capabilities of modern reinforcement learning to discover the causal structure of a synthetic environment. The study separates cases where the agents can only observe or they can also act, showing the expected gains of active intervention. The experiments are so far synthetic, but it would be really interesting to see how the lessons learned extend to more realistic environments. It would also be very nice to have a sequence of increasingly complex synthetic environments where causal inference is the task of interest, such that we can compare the performance of different RL algorithms in this task (the authors only used one). I would change the title to "Causal Reasoning from Reinforcement Learning", since "meta-learning" is an over-loaded term and I do not clearly see its prevalence on this submission.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Causal Reasoning from Meta-reinforcement learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data. Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions. Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches. More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This submission is an great ablation study on the capabilities of modern reinforcement learning to discover the causal structure of a synthetic environment. The study separates cases where the agents can only observe or they can also act, showing the expected gains of active intervention. The experiments are so far synthetic, but it would be really interesting to see how the lessons learned extend to more realistic environments. It would also be very nice to have a sequence of increasingly complex synthetic environments where causal inference is the task of interest, such that we can compare the performance of different RL algorithms in this task (the authors only used one). I would change the title to "Causal Reasoning from Reinforcement Learning", since "meta-learning" is an over-loaded term and I do not clearly see its prevalence on this submission.
assistant
 This submission is an great ablation study on the capabilities of modern reinforcement learning to discover the causal structure of a synthetic environment. The study separates cases where the agents can only observe or they can also act, showing the expected gains of active intervention. The experiments are so far synthetic, but it would be really interesting to see how the lessons learned extend to more realistic environments. It would also be very nice to have a sequence of increasingly complex synthetic environments where causal inference is the task of interest, such that we can compare the performance of different RL algorithms in this task (the authors only used one). I would change the title to "Causal Reasoning from Reinforcement Learning", since "meta-learning" is an over-loaded term and I do not clearly see its prevalence on this submission.