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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Finding and Listing Front-door Adjustment Sets
Authors: Hyunchai Jeong, Jin Tian, Elias Bareinboim
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present algorithms for finding and enumerating possible sets satisfying the FD criterion in a given causal diagram. 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Researcher Affiliation | Academia | Hyunchai Jeong Purdue University EMAIL Jin Tian Iowa State University EMAIL Elias Bareinboim Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 FINDFDSET (G, X, Y, I, R) Algorithm 2 LISTFDSETS (G, X, Y, I, R) Figure 2: A function that outputs the set of candidate variables satisfying the second condition of the FD criterion. Figure 3: A function that outputs the set of candidate variables potentially satisfying the second and third conditions of the FD criterion. Figure 4: A function that facilitates the construction of a set that satis๏ฌes the third condition of the FD criterion. |
| Open Source Code | Yes | 1Code is available at https://github.com/Causal AILab/Frontdoor Adjustment Sets. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and theoretical properties (correctness, complexity). It does not involve empirical studies with data, training, or validation, as indicated by '[N/A]' for experimental questions in the ethics review. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm design and theoretical properties. It does not involve empirical studies with data, training, or validation, and therefore no dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs. It does not report on empirical experiments, and thus no hardware specifications for running experiments are provided. The ethics review states '[N/A]' for questions about compute resources. |
| Software Dependencies | No | The paper focuses on theoretical algorithm design and does not specify software dependencies with version numbers (e.g., specific libraries, frameworks, or their versions) required for replication or implementation of the algorithms. |
| Experiment Setup | No | The paper is theoretical, describing algorithms and their properties, and does not conduct empirical experiments. Therefore, it does not provide details regarding experimental setup, such as hyperparameters or training configurations. |