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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
Authors: Juan Correa, Sanghack Lee, Elias Bareinboim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, building on a more explicit definition of nested counterfactuals, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones. Second, we introduce a sufficient and necessary graphical condition for counterfactual identification from an arbitrary combination of observational and experimental distributions. Lastly, we develop an efficient and complete algorithm for identifying nested counterfactuals; failure of the algorithm returning an expression for a query implies it is not identifiable. |
| Researcher Affiliation | Academia | Juan D. Correa Columbia University EMAIL Sanghack Lee Seoul National University EMAIL Elias Bareinboim Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 CTFIDU(Y , y , Z, G) and Algorithm 2 CTFID(Y , y , X , x , Z, G) |
| Open Source Code | No | The paper does not provide a statement or link for open-source code specific to the methodology described. It references a technical report [5] but does not explicitly state code availability. |
| Open Datasets | No | This is a theoretical paper focusing on identifiability conditions and algorithms, not on empirical studies with datasets. Therefore, no training data or access information is provided. |
| Dataset Splits | No | This is a theoretical paper, and thus it does not mention validation dataset splits. |
| Hardware Specification | No | The paper describes theoretical concepts and algorithms, not empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers, as it does not report on empirical experiments. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithms and mathematical proofs, not on specific experimental setups or hyperparameter values. |