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].
Counterfactual Transportability: A Formal Approach
Authors: Juan D Correa, Sanghack Lee, Elias Bareinboim
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Specifically, we introduce a sufficient and necessary graphical condition and develop an efficient, sound, and complete algorithm for transporting counterfactual quantities across domains in nonparametric settings. Failure of the algorithm implies the impossibility of generalizing the target counterfactual from the available data without further assumptions. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Universidad Aut onoma de Manizales, Manizales, Colombia 2Graduate School of Data Science, Seoul National University, Seoul, South Korea 3Department of Computer Science, Columbia University, New York, USA. |
| Pseudocode | Yes | Algorithm 1 SIMPLIFY(Y , y ) ... Algorithm 2 CTFTRU(Y , y , Z, G ) ... Algorithm 3 CTFTR(Y , y , X , x , Z, G ) ... Algorithm 4 σ-TR(Ci, Z, G ) ... Algorithm 5 IDENTIFY(C, T, Q, G) |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve empirical evaluation with datasets, hence no information about publicly available training data is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical evaluation with datasets, hence no information about training/test/validation splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers, thus none are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not describe any experiments, thus no experimental setup details like hyperparameters or training settings are provided. |