Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Topological Perspective on Causal Inference
Authors: Duligur Ibeling, Thomas Icard
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a topological learning-theoretic perspective on causal inference by introducing a series of topologies defined on general spaces of structural causal models (SCMs). As an illustration of the framework we prove a topological causal hierarchy theorem, showing that substantive assumption-free causal inference is possible only in a meager set of SCMs. Thanks to a known correspondence between open sets in the weak topology and statistically verifiable hypotheses, our results show that inductive assumptions sufficient to license valid causal inferences are statistically unverifiable in principle. |
| Researcher Affiliation | Academia | Duligur Ibeling Department of Computer Science Stanford University EMAIL Thomas Icard Department of Philosophy Stanford University EMAIL |
| Pseudocode | No | The paper focuses on mathematical proofs and definitions; it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific datasets for training or other purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (training, validation, or test) as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |