A Topological Perspective on Causal Inference
Authors: Duligur Ibeling, Thomas Icard
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 duligur@stanford.edu Thomas Icard Department of Philosophy Stanford University icard@stanford.edu |
| 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. |