Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Fixed-Point Approach for Causal Generative Modeling
Authors: Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct an extensive evaluation of each method individually, and show that when combined, our model outperforms various baselines on generated out-of-distribution problems. |
| Researcher Affiliation | Industry | Work done when Joel Jennings and Cheng Zhang were affiliated with Microsoft Research. 1Microsoft Research 2Google Deep Mind. Correspondence to: Meyer Scetbon <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 d-TOE(M, (Dtr, Gtr)) |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the described methodology. It only references implementations for baselines and other related works. |
| Open Datasets | Yes | We reproduce the procedure proposed in (Lorch et al., 2022) to generate synthetic datasets and their associated DAGs using randomly sampled SCMs. |
| Dataset Splits | Yes | Given a dataset D Rntot d where ntot is the total number of samples, we split it into three datasets w.r.t the sample size, with ratio 0.8, 0.1, 0.1 for training, validation and testing respectively. |
| Hardware Specification | Yes | We use 4 A100 GPUs with a total of 320 Gi B of memory and 85 CPUs to train our architecture M. |
| Software Dependencies | No | The paper mentions "the Adam implementation of Pytorch (Paszke et al., 2017)" but does not provide specific version numbers for Pytorch or any other critical software dependencies. |
| Experiment Setup | Yes | We consider TANM with an embedding dimension of D = 128, and L = 2 layers. The causal attention mechanism uses 8 heads with an embedding dimension of dhead = 32. We do not hyper-tune the model on each specific instance, but use the same training configuration for all experiments. See appendix E for details. |