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..
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. |