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..
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
Authors: Daniel Kumor, Carlos Cinelli, Elias Bareinboim
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a polynomial-time algorithm for identification of structural coefficients in linear causal models that subsumes previous efficient state-of-the-art methods, unifying several disparate approaches to identification in this setting. Building on these results, we develop a procedure for identifying total causal effects in linear systems. |
| Researcher Affiliation | Academia | 1Dept. of Computer Science, Purdue University, West Lafayette, IN, USA 2Dept. of Statistics, University of California, Los Angeles, CA, USA 3Dept. of Computer Science, Columbia University, New York, NY, USA. |
| Pseudocode | Yes | Algorithm 1 AC: is given a graph, target vertex x, a set of candidate instruments (which can themselves be AVs), a set of identified structural parameters, and returns the Auxiliary Cutset for x Algorithm 2 ACID: Given a graph, returns a set of identifiable structural parameters. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments or their setup details, such as hyperparameters or training configurations. |