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..
DiffPO: A causal diffusion model for learning distributions of potential outcomes
Authors: Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal, Stefan Feuerriegel
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Across a wide range of experiments, we show that our method achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal & Stefan Feuerriegel LMU Munich Munich Center for Machine Learning EMAIL |
| Pseudocode | No | The paper describes processes and equations but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at https://github.com/yccm/Diff PO. |
| Open Datasets | Yes | We thus follow prior literature (e.g.,[22, 38]) and benchmark our model using synthetic datasets. ... We estimate the CATE across ACIC 2016 & ACIC 2018, which are widely used dataset collections for CATE benchmarking [66, 12, 42]. ... IHDP dataset. This is a semi-synthetic dataset from the Infant Health and Development Program (IHDP) [25] |
| Dataset Splits | Yes | We use a ten-fold split for train/test samples (80%/20%). ... We use five random train/test splits (80% / 20%) for each dataset, tune hyperparameters on the first split, and evaluate the average out-sample on every split. |
| Hardware Specification | Yes | Experiments were carried out on 1 NVIDIA A100-PCIE-40GB. |
| Software Dependencies | No | We implemented our Diff PO in Py Torch. However, no specific version numbers for PyTorch or other software dependencies are provided. |
| Experiment Setup | Yes | The number of diffusion sampling steps is 100. Training is conducted with a batch size of 256 and a learning rate of 0.0005. |