Budgeted Experiment Design for Causal Structure Learning

Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim

ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions.
Researcher Affiliation Academia 1Department of ECE, and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA 2Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran 3Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Pseudocode Yes Algorithm 1 General Greedy Algorithm; Subroutine 1 Unbiased D(I) Estimator Subroutine; Subroutine 2 Fast D(I) Estimator Subroutine; Algorithm 2 Improved Greedy Algorithm
Open Source Code No The paper does not provide an explicit statement or link to open-source code for the described methodology.
Open Datasets Yes We considered GRNs in DREAM 3 In Silico Network challenge, conducted in 2008 (Marbach et al., 2009).
Dataset Splits No The paper mentions generating synthetic graphs and using existing GRN datasets but does not provide specific details on train/validation/test dataset splits, percentages, or methodology for partitioning the data.
Hardware Specification Yes For the aforementioned setting, the running time of the proposed approach on a machine with Intel Core i7 processor and 16 GB of RAM was 216 seconds while the one of the brute force approach was greater than 6000 seconds.
Software Dependencies No The paper does not provide specific software names with version numbers or reproducible details of ancillary software.
Experiment Setup Yes We generated 100 instances of chordal DAGs of order 20. ... We evaluated the discovered edge ratio for budget k = 3 on graphs with order n {10, 15, 20, 25, 30}.