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 Sampling of Atomic Interventions from Observations
Authors: Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of efficiently estimating the effect of an intervention on a single variable (atomic interventions) using observational samples in a causal Bayesian network. Our goal is to give algorithms that are efficient in both time and sample complexity in a non-parametric setting. We design sample and time efficient algorithms for the above-mentioned estimation problems. We present an efficient algorithm for the evaluation and generation problems. Finally we establish a lower bound for the sample complexity showing that our sample complexity has optimal dependence on the parameters n and ε, as well as if k = 1 on the strong positivity parameter. |
| Researcher Affiliation | Academia | 1National University of Singapore 2Cornell University 3University of Wisconsin-Madison 4University of Nebraska-Lincoln. |
| Pseudocode | Yes | Algorithm 1 Learning Dx |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology described in this paper was found. The paper mentions other open-source tools like 'causality', 'Do Why', and 'CIBN' in the related work section, but not for their own work. |
| Open Datasets | No | No specific publicly available dataset is mentioned or linked. The paper refers to 'samples from the observational distribution P' in a theoretical context. |
| Dataset Splits | No | No specific dataset split information (training, validation, or testing) is provided, as the paper presents theoretical algorithms and analyses rather than empirical evaluations. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory) used for running experiments were found. The paper focuses on theoretical algorithm design and analysis. |
| Software Dependencies | No | No specific ancillary software details with version numbers (e.g., library or solver names) are provided for the algorithms developed in this paper. |
| Experiment Setup | No | No specific experimental setup details, such as hyperparameter values, training configurations, or system-level settings, are provided. The paper is theoretical and focuses on algorithm design and analysis. |