Causal Inference via Kernel Deviance Measures
Authors: Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on real-world time series data and the real-world benchmark dataset Tübingen Cause-Effect Pairs where we outperform state-of-the-art approaches. 4 Experimental Results |
| Researcher Affiliation | Collaboration | Jovana Mitrovic Dino Sejdinovic Yee Whye Teh Department of Statistics, University of Oxford [mitrovic, dino.sejdinovic, y.w.teh]@stats.ox.ac.uk Now at Deep Mind, UK. |
| Pseudocode | No | Algorithms summarizing our causal inference methodology are given in the supplementary material. |
| Open Source Code | No | The paper mentions that 'Algorithms summarizing our causal inference methodology are given in the supplementary material', but does not explicitly state that the code is open-source or provide a link for its own implementation. |
| Open Datasets | Yes | For this purpose, we test KCDC on the only widely used benchmark dataset Tübingen Cause-Effect Pairs (TCEP) [15]. This dataset is comprised of real-world cause-effect samples that are collected across very diverse subject areas with the true causal direction provided by human experts. J. M. Mooij, D. Janzing, J. Zscheischler, and B. Schölkopf. Cause-effect pairs repository. 2015. http://webdav.tuebingen.mpg.de/cause-effect/. |
| Dataset Splits | No | The paper describes using synthetic data and the TCEP dataset, but does not provide specific details on train/validation/test splits (percentages, counts, or explicit splitting methodology) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or specific cloud instances used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with specific versions, or solver versions) needed to replicate the experiments. |
| Experiment Setup | Yes | We report the results when using the log kernel on the input and the rational quadratic kernel on the response. both on the response and input we used either a log kernel k(x, x ) = log( x x q + 1) with q in [2, 3, 4] or an RBF kernel with bandwidth [1, 1.5, 2] times the median heuristic. |