SlowGuess/NeurIPS_2023_OCR huggingface dataset:
for the variant YER124C without contamination by irrelevant genes. The estimated causal graph and causal effects as well as more detailed analyses are provided in App. E.2. These observations align with findings from our simulation studies, further supporting NSCSL's superiority in revealing important causal features.
7 Limitations and Future Research
In this work, we introduced NSCSL that leverages causal effects/POCs to systematically assess feature importance while learning a causal graph. By identifying a subgraph closely related to the outcome, our method filters irrelevant variables, presenting a significant advancement in the field. Extensive empirical evaluations on simulated and real-world data underscore NSCSL's superior performance over existing algorithms, including important findings on yeast genes and the protein signaling network. However, this promising advancement is not without limitations. First, NSCSL, like most existing causal structural learning methods, assumes no unmeasured confounders (A2) and the causal Markov condition. These assumptions may not hold in practice, leading to biased causal effect estimates and potential errors in the causal graph. Second, NSCSL employs absolute causal effects as a substitute for POCs to facilitate estimation in high-dimensional settings. Although theoretically consistent under certain conditions, examining the differences between these two methods in general feature and outcome spaces is an area for future research.
Research reported in this publication was supported in part by the Amazon Web Services (AWS) Cloud Research program, the Office of Naval Research under grant N00014-23-1-2590, and the National Science Foundation under grant No. 2231174 and No. 2310831. The authors thank the area chair and the reviewers for their constructive comments which have led to a significant improvement of the earlier version of this article.
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