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..
Learning and Testing Causal Models with Interventions
Authors: Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main highlight of our work is that we prove bounds on the number of samples, interventions, and time steps required by our algorithms. Our algorithms are enabled by a new subadditivity inequality for the squared Hellinger distance between two causal Bayesian networks. |
| Researcher Affiliation | Academia | Jayadev Acharya School of ECE Cornell University EMAIL Arnab Bhattacharyya National University of Singapore & Indian Institute of Science EMAIL Constantinos Daskalakis EECS MIT EMAIL Saravanan Kandasamy STCS Tata Institute of Fundamental Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm for C2ST(G, ϵ) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is being released. |
| Open Datasets | No | This paper is theoretical, focusing on algorithms and complexity bounds, and does not involve empirical evaluation on datasets. Therefore, it does not specify public dataset availability. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical evaluation on datasets, thus it does not specify training/validation/test splits. |
| Hardware Specification | No | As the paper presents theoretical work without empirical experiments, it does not describe any hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and their complexity, and does not include details on software dependencies with specific version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, including hyperparameters or system-level training settings, as it does not conduct empirical experiments. |