Entropic Causal Inference: Identifiability and Finite Sample Results
Authors: Spencer Compton, Murat Kocaoglu, Kristjan Greenewald, Dmitriy Katz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct several experiments to evaluate the robustness of the framework. Complete details of each experiment are provided in the supplementary material. |
| Researcher Affiliation | Collaboration | Spencer Compton MIT MIT-IBM Watson AI Lab scompton@mit.edu Murat Kocaoglu MIT-IBM Watson AI Lab IBM Research murat@ibm.com Kristjan Greenewald MIT-IBM Watson AI Lab IBM Research kristjan.h.greenewald@ibm.com Dmitriy Katz MIT-IBM Watson AI Lab IBM Research dkatzrog@us.ibm.com |
| Pseudocode | No | The paper describes methods and algorithms (e.g., "greedy minimum entropy coupling algorithm"), but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or a direct link to the source code for the methodology described. |
| Open Datasets | Yes | Finally, we apply the algorithm on Tübingen cause-effect pairs dataset. Tübingen Cause-Effect Pairs In [15], authors employed the total entropy-based algorithm on Tübingen data [20] and showed that it performs similar to additive noise models with an accuracy of 64%. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states that 'Complete details of each experiment are provided in the supplementary material' but does not include specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |