Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The manuscript proposed a feature selection model for the causal discovery task based on the optimization of multi-dimensional transfer entropy of the input data by combinatorial stochastic gates. The proposed model was evaluated on by synthetic time series datasets with the presence of latent process, synthetic spatial progression of the scRNA-seq data, as well as the real (pancreatic endocrinogenesis trajectory) data. Experiment results indicate superior performance of the model over both classic and recent causal discovery methods. 
strength_and_weaknesses: Strength: The methodology involved in the optimized feature subset selection for causal discovery (Theorem 3.1, 3.4) and the design of the stochastic gate-based approach is important to the field of data mining. Experiment results show superior performance on both synthetic and real data, further validating the effectiveness of the model.
 
Weakness: First of all, in section 3.2, the author mentioned that “GEASS provides both outputs of active features and embeddings produced by causally interacting features. In this paper, we emphasize the use of the former as the latter embedding output may be complex and nonlinear, potentially requiring additional architectures to maximize its interpretability.” The statement is confusing as it did not specify where the “embeddings produced by causally interacting features” were obtained: whether they were from the stochastic gates, or the MLPs indicated in Fig. 2? Further, there are no explanations for the design and purpose of the MLPs in Fig. 2.  Also, the term “STG layer” in Fig. 2 has never been mentioned throughout the manuscript. The reviewer guesses it is referring to the stochastic gates but is not sure about it. 

clarity,_quality,_novelty_and_reproducibility: Clarity: The manuscript is clearly written, although a full understanding of the algorithm details would need a frequent reference to the appendix and cited literature. 

Quality: The quality of the manuscript is good, with an extensive investigation of the algorithm design, proof of the key theorems, and insights into the model development. 

Reproducibility: The manuscript did not provide any code repositories associated with the model. With the current description of the methodology, the work can be potentially reproduced, yet not guaranteed. 


summary_of_the_review: A causal discovery framework with fundamental and important algorithmic development (Theorem 3.1 and 3.4), with good practical value as well.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors introduce a framework of causal discovery in data using two steps: a causal feature selection in a general graph structured data and causal graph identification. To do the former, the authors define a multi-dimensional transfer entropy maximization metric and device ways to optimise it. They test out the framework in single cell and spatial data.
strength_and_weaknesses: The problem of detecting causal features in spatial transcriptomics is an important problem. The authors promote a nice framework and benchmark on a variety of datasets. 
clarity,_quality,_novelty_and_reproducibility: It is quite well written.
summary_of_the_review: The paper addresses an interesting problem. The main idea is neat and is benchmarked sufficiently on quite a lot of data. Thus I recommend acceptance.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper provides a novel methodology for identifying significant causal features in spatiotemporal data, with primary applications in biology (e.g. scRNA-Seq data). Its primary novelty lies in the judicious use of a transfer entropy characterisation of feature significance/relevance and the design of a combinatorial stochastic gate layer.
strength_and_weaknesses: The strengths are as follows:

The paper is well-written and coherently organised.

It has clear novelty in the use of maximum transfer entropy.

All the non-trivial mathematical statements are supported by proofs in the appendix.

The weaknesses are as follows:

It is unclear how the forward operator translates to spatial settings; the authors vaguely mention something to do with diffusion, but that would not be applicable unless the diffusion trajectory could be reconstructed in time.

It is also unclear how the mutual information is computed (via approximation? based on frequencies in the data?).

Many proofs are hand-wavy; this may make them clear/obvious to experts, but for the general reader they should be made more explicit.

Some of the extensions of the method to specific datasets seem somewhat ad hoc, rather than being motivated by the same principles as the main method.
clarity,_quality,_novelty_and_reproducibility: The paper is clearly written, with high-quality exposition and proofs supporting each non-trivial assertion. The methods appear to be novel, but I am not very familiar with this subfield. The results are well-described, but no code repository is provided, so reproducibility is limited.
summary_of_the_review: The paper is an advance over the current state of the art and provides interesting results in several biological applications.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper identifies causally interacting features of high dimensional temporal/spatial data by considering the sparsity of underlying causal mechanisms instead of link sparsity, which can select the critical corresponding features for downstream causal discovery. Theoretical studies are provided, and empirical evaluations on synthetic and real biological data showed its superiority over competing methods. 
strength_and_weaknesses: Strengths: 1. This paper proposes a novel solution of causal feature selection problem in general graph-structured data. 
                  2. Theoretical analysis is provided.
                  3. Empirical results showed outperformed performance. 

Weaknesses: 1. I think the current version takes too much space on the theoretical analysis while ignoring the model itself. Figure 2 is not clear to me. 
                      2. For the Merfish data experiment, what's the reason to downsample the gene number from 4000 to 1000? In real situations, the gene number can be millions. If the proposed method can not deal with high-dimensional features, then the scalability will be an issue. 
                      3. What's the criteria for GEASS to select 9 genes in Merfish data?  Can the authors elaborate more /give insight on interpreting the selection of this subset for biological discovery?
                     
clarity,_quality,_novelty_and_reproducibility: The code is not provided. See above for my questions.
summary_of_the_review: I think this is a solid work.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: All reviewers agree that the paper offers a novel practical method for extracting causal features from high dimensional spatiotemporal data, based on granger causality; wtih applications in biology.
justification_for_why_not_higher_score: There could be limitations in its applicability in other biological data sets beyond single cell rna, due to the spatiotemporal nature of the technique
justification_for_why_not_lower_score: The method is novel with theoretical guarantees and practical impact
Decision: Accept: notable-top-25%