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..
Discovering Mixtures of Structural Causal Models from Time Series Data
Authors: Sumanth Varambally, Yian Ma, Rose Yu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method surpasses state-of-the-art benchmarks in causal discovery tasks through extensive experimentation on synthetic and real-world datasets, particularly when the data emanates from diverse underlying causal graphs. Experimentally, we demonstrate the strong performance of our method on both synthetic and real-world datasets. Notably, MCD can accurately infer the mixture causal graphs and mixture membership information, even when the number of SCMs is misspecified. |
| Researcher Affiliation | Academia | 1Halıcıo glu Data Science Institute, University of California, San Diego, La Jolla, USA 2Department of Computer Science and Engineering, University of California, San Diego, La Jolla, USA. Correspondence to: Sumanth Varambally <EMAIL>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation is available at https: //github.com/Rose-STL-Lab/MCD. |
| Open Datasets | Yes | The Netsim benchmark dataset (Smith et al., 2011) and The DREAM3 dataset (Prill et al., 2010) and We use the yahoofinancials package to retrieve the daily closing prices of D = 100 stocks from January 1, 2016 to July 1, 2023. |
| Dataset Splits | Yes | We train the model on 80% of the data and validate on the remaining 20%. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'Py Torch vectorization' and the 'yahoofinancials package', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train the model on 80% of the data and validate on the remaining 20%. The time series length T is 100, and the time lag L is set to 2 for all the methods... Table 3 summarizes the hyperparameters used for training. This includes: Matrix LR 10e-2, Likelihood LR 10e-3, Batch Size 128, # Outer auglag steps 100, # Max inner auglag steps 6000, Embedding dim e = D, Sparsity factor λ 5, Spline type Quadratic/Linear. |