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 [1].
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. |