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 Instrumental Cutsets
Authors: Daniel Kumor, Bryant Chen, Elias Bareinboim
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate graphical conditions to allow efficient identification in arbitrary linear structural causal models (SCMs). In particular, we develop a method to efficiently find unconditioned instrumental subsets... Further, we prove that determining whether an effect can be identified with TSID (Weihs et al., 2017), a method more powerful than unconditioned instrumental sets and other efficient identification algorithms, is NP-Complete. Finally, building on the idea of flow constraints, we introduce a new and efficient criterion called Instrumental Cutsets (IC), which is able to solve for parameters missed by all other existing polynomial-time algorithms. |
| Researcher Affiliation | Collaboration | Daniel Kumor Purdue University EMAIL Bryant Chen Brex Inc. EMAIL Elias Bareinboim Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1 Find Maximal Match-Block given DAG G, source nodes S and target nodes T; Algorithm 2 IC solves for edges incoming to y given a set of known edges Λ |
| Open Source Code | Yes | A Python implementation is available at https://github.com/dkumor/instrumental-cutsets |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving public datasets or data access information. |
| Dataset Splits | No | The paper is theoretical and does not mention dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not specify hardware used for experiments. |
| Software Dependencies | No | The paper mentions a 'Python implementation' but does not provide specific version numbers for Python or any required libraries/solvers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |