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 [1].
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
Authors: Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Second, we develop an algorithm capable of harnessing the collection of data to learn the corresponding equivalence class. We then prove that this algorithm is sound and complete, in the sense that it is the most informative in the sample limit, i.e., it discovers as many tails and arrowheads as can be oriented within a Ψ-Markov equivalence class. |
| Researcher Affiliation | Collaboration | Amin Jaber Department of Computer Science Purdue University, USA EMAIL Murat Kocaoglu MIT-IBM Watson AI Lab IBM Research MA, USA EMAIL Karthikeyan Shanmugam MIT-IBM Watson AI Lab IBM Research NY, USA EMAIL Elias Bareinboim Department of Computer Science Columbia University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Ψ-FCI: Algorithm for Learning a Ψ-PAG |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments run on a dataset, thus there is no mention of publicly available or open datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data or experiments, so there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any implemented system or experiment, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |