Learning and Sampling of Atomic Interventions from Observations
Authors: Arnab Bhattacharyya, Sutanu Gayen, Saravanan Kandasamy, Ashwin Maran, Vinodchandran N. Variyam
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |