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].
A Calculus for Stochastic Interventions:Causal Effect Identification and Surrogate Experiments
Authors: Juan Correa, Elias Bareinboim10093-10100
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Specifically, in this paper, we introduce a new set of inference rules (akin to do-calculus) that can be used to derive claims about general interventions, which we call σ-calculus. We develop a systematic and efficient procedure for finding estimands of the effect of general policies as a function of the available observational and experimental distributions. We then prove that our algorithm and σ-calculus are both sound for the tasks of identification (Pearl, 1995) and z-identification (Bareinboim and Pearl, 2012) under this class of interventions. |
| Researcher Affiliation | Academia | Juan D. Correa, Elias Bareinboim Computer Science Department Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 σ-IDENTIFY(Y, W, σX, Z, G) |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the methodology is openly available. |
| Open Datasets | No | This is a theoretical paper presenting a calculus and an algorithm. It does not involve empirical training on datasets. |
| Dataset Splits | No | This is a theoretical paper presenting a calculus and an algorithm. It does not involve empirical validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper describes a theoretical calculus and an algorithm but does not mention any specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | This is a theoretical paper describing a calculus and an algorithm, and thus does not include details on an experimental setup, hyperparameters, or training settings. |