The Causal-Neural Connection: Expressiveness, Learnability, and Inference
Authors: Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations corroborate the proposed approach. In Sec. 5, we perform experiments with one possible implementation which support the feasibility of the proposed approach. |
| Researcher Affiliation | Collaboration | Kevin Xia Causal AI Lab Columbia University kmx2000@columbia.edu Kai-Zhan Lee Bloomberg L.P. Columbia University kl2792@columbia.edu Yoshua Bengio MILA Université de Montréal yoshua.bengio@mila.quebec Elias Bareinboim Causal AI Lab Columbia University eb@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1: Identifying/estimating queries with NCMs. Algorithm 2: Training Model |
| Open Source Code | No | The paper does not contain an explicit statement or a direct link to the source code for the methodology described in the paper. It only references a third-party library 'pytorch-made' [36]. |
| Open Datasets | No | Observational data is generated from 8 different SCMs. The paper describes generating data but does not specify a publicly available or open dataset with concrete access information (link, DOI, formal citation). |
| Dataset Splits | No | The paper mentions training epochs and evaluating performance but does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or testing sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper refers to general categories like "gradient descent tools" and mentions "pytorch-made" but does not provide specific version numbers for these or other software dependencies necessary for replication. |
| Experiment Setup | Yes | The parameter λ is set to 1 at the beginning, and decreases logarithmically over each epoch until it reaches 0.001 at the end of training. The classification accuracies per training epoch are shown in Fig. 4 (middle row) over 3000 training epochs. We rely on a hypothesis testing step such as |f(c M(θmax)) f(c M(θmin))| < τ for quantity of interest f and a certain threshold τ, with τ = 0.01 (blue), 0.03 (green), 0.05 (red). |