Experimental Design for Learning Causal Graphs with Latent Variables
Authors: Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose an efficient randomized algorithm that can learn the observable graph using O(d log2 n) interventions where d is the degree of the graph. We further propose an efficient deterministic variant which uses O(log n + l) interventions, where l is the longest directed path in the graph. Next, we propose an algorithm that uses only O(d2 log n) interventions that can learn the latents between both nonadjacent and adjacent variables. |
| Researcher Affiliation | Collaboration | Murat Kocaoglu Department of Electrical and Computer Engineering The University of Texas at Austin, USA mkocaoglu@utexas.edu Karthikeyan Shanmugam IBM Research NY, USA karthikeyan.shanmugam2@ibm.com Elias Bareinboim Department of Computer Science and Statistics Purdue University, USA eb@purdue.edu |
| Pseudocode | Yes | Algorithm 1 Learn Ancestral Relations, Algorithm 2 Learn Observable Graph/Deterministic, Algorithm 3 Learn Observable/Randomized, Algorithm 4 Learn Latent Non Edge, Algorithm 5 Learn Latent Edge |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any dataset for training or evaluation, nor does it provide concrete access information for a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers needed to replicate any experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameter values or training configurations. |