Nested Counterfactual Identification from Arbitrary Surrogate Experiments
Authors: Juan Correa, Sanghack Lee, Elias Bareinboim
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 jdcorrea@cs.columbia.edu Sanghack Lee Seoul National University sanghack@snu.ac.kr Elias Bareinboim Columbia University eb@cs.columbia.edu |
| 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. |