Discovering Mixtures of Structural Causal Models from Time Series Data
Authors: Sumanth Varambally, Yian Ma, Rose Yu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <svarambally@ucsd.edu>. |
| 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. |