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..
Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference
Authors: Anpeng Wu, Haiyi Qiu, Zhengming Chen, Zijian Li, Ruoxuan Xiong, Fei Wu, Kun Zhang
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two widely-used benchmarks demonstrate the effectiveness and superiority of Cau Gramer. |
| Researcher Affiliation | Academia | 1Zhejiang University 2MBZUAI 3Guangdong University of Technology 4Emory University 5Carnegie Mellon University |
| Pseudocode | Yes | The pseudo-code is placed in Algorithm 1. |
| Open Source Code | Yes | The code is available at https://github.com/anpwu/Cau Gramer. |
| Open Datasets | Yes | The Blog Catalog and Flickr datasets are available at: https://github.com/songjiang0909/Causal-Inference-on-Networked-Data. |
| Dataset Splits | Yes | Jiang & Sun (2022) use METIS (Karypis & Kumar, 1998) to partition the original networks into three sub-networks as train/valid/test data with 2482, 2461, and 2358 samples in Flickr, and 1784, 1716, and 1696 samples in Blog Catalog. |
| Hardware Specification | Yes | Hardware used: (1) Mac Book Pro with Apple M2 Pro. (2) Ubuntu 16.04.3 LTS operating system with 2 * Intel Xeon E5-2660 v3 @ 2.60GHz CPU (40 CPU cores, 10 cores per physical CPU, 2 threads per core), 256 GB of RAM, and 4 * Ge Force GTX TITAN X GPU with 12GB of VRAM. |
| Software Dependencies | Yes | Software used: Python 3.9 with numpy 1.26.4, scipy 1.13.0, pandas 2.2.2, torch 2.3.0, scikit-learn 1.4.2, openpyxl 3.1.2, torch geometric 2.5.2, torch-scatter 1.1.0. |
| Experiment Setup | Yes | In this paper, we propose a L-layers (default: 2) M-heads (default: 3) cross-attention GCN to learn the representation Rx = gx(x, A). In each attention head, all neural networks consist of one layer comprising 32 hidden units. We then perform cross-attention computation, which yields the concatenation of M head embeddings, followed by a feed-forward network to output a 32-dimensional representation. ... Then, we use three two-layer linear networks, where each layer comprises 64 hidden units, to regress treatments T and potential outcomes {Y0, Y1}. ... we use the ReLU activation function and set the dropout rate to 0.1 to mitigate overfitting. Then, the objective function is: ... Then, we adopt Adam optimization with a learning rate of 0.01 and set epochs to 300 to alternately train WA and { ˆT , ˆY0, ˆY1}. |