Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Experimental Design for Learning Causal Graphs with Latent Variables
Authors: Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,379 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,736 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Karthikeyan Shanmugam IBM Research NY, USA EMAIL Elias Bareinboim Department of Computer Science and Statistics Purdue University, USA EMAIL |
| 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. |