Causal Identification under Markov Equivalence: Completeness Results
Authors: Amin Jaber, Jiji Zhang, Elias Bareinboim
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive a complete algorithm for identification given a PAG. This implies that whenever the causal effect is identifiable, the algorithm returns a valid identification expression; alternatively, it will throw a failure condition, which means that the effect is provably not identifiable. We further provide a graphical characterization of nonidentifiability of causal effects in PAGs. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, West Lafayette, USA 2Department of Philosophy, Lingnan University, NT, HK. |
| Pseudocode | Yes | Algorithm 1 IDP(x, y) given PAG P |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical contributions (algorithm design, proofs) and does not describe experimental training on datasets. Therefore, it does not mention public datasets or provide access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or data splitting. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware 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 an experimental setup with hyperparameters or training settings. |