Kati8/ICLR_2023_2025 huggingface dataset:
authors: Sorawit Saengkyongam
authors: Elan Rosenfeld
authors: Pradeep Kumar Ravikumar
authors: Niklas Pfister
authors: Jonas Peters
reviews: 
Comments: The paper discusses identification strategies for effect extrapolation when the treatment of interest is unobserved during the experiment. The key assumptions are that 

- (exogenous) treatment A affects outcome Y through and only through an unobserved mediator Z
- can observe a rich feature $X=g_0(Z)$ that is and only is a (injective) function of Z
- the relationship between A and Z is linear

Together with some other regularity assumptions, the author shows that, given an encoder that aff-identifies $g_0^{-1}$, it is possible to identify $E[Y|do(A=a^*)]$ and $E[Y|X=x,do(A=a^*)]$ for a treatment $a^*$ that has never been observed. They also propose a method for locating such an encoder.
Strengths: - The paper studies intervention extrapolation under a well-chosen set of assumptions, which I find more appealing than the previously studied scenarios in the literature.

- To the best of my knowledge, the proposed identification strategy is novel.

- The manuscript is clear and well-written. 

- The proposed algorithm is straightforward and practical.
Weaknesses: - The assumptions are clear mathematically but might seem opaque to readers unfamiliar with the literature. The authors may want to give an example of what some of the key assumptions would imply in a simple setup.

- Many of the structural assumptions are not testable, and it is unclear to me when one shall be comfortable using the proposed method.

Also see questions.
Recommendation: 8: accept, good paper
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 4 excellent