Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
Authors: Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan CHAN, Yanhui Geng
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data demonstrate the effectiveness of our proposed approach. In this section, experimental results on both synthetic and real data are given to show the performance of ANM-MM on causal inference and mechanism clustering tasks. |
| Researcher Affiliation | Collaboration | The Chinese University of Hong Kong; Huawei Noah s Ark Lab; Huawei Montréal Research Center {sbhu, lwchan}@cse.cuhk.edu.hk {chenzhitang2, vahid.partovinia, geng.yanhui}@huawei.com |
| Pseudocode | Yes | Algorithm 1: Causal Inference; Algorithm 2: Mechanism clustering |
| Open Source Code | Yes | The Python code of ANM-MM is available online at https: //github.com/amber0309/ANM-MM. |
| Open Datasets | Yes | Causal inference on Tüebingen cause-effect pairs. We evaluate the causal inference performance of ANM-MM on real world benchmark cause-effect pairs3 [15]. (Footnote 3: https://webdav.tuebingen.mpg.de/cause-effect/.) Clustering on BAFU air data. We evaluate the clustering performance of ANM-MM on real air data obtained online5. (Footnote 5: https://www.bafu.admin.ch/bafu/en/home/topics/air.html) |
| Dataset Splits | No | The paper describes synthetic data generation and sampling for real data, but it does not specify explicit train/validation/test splits (e.g., 80/10/10 split or k-fold cross-validation) for reproduction. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Python code' but does not specify version numbers for Python or any other software libraries or dependencies. |
| Experiment Setup | Yes | ANM-MM was applied using different parameter λ {0.001, 0.01, 0.1, 1, 10} and IGCI was applied using different reference measures and estimators. To find Θ, we resort to the gradient descant methods. |