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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Learning Representations of Instruments for Partial Identification of Treatment Effects
Authors: Jonas Schweisthal, Dennis Frauen, Maresa Schröder, Konstantin Hess, Niki Kilbertus, Stefan Feuerriegel
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 26,867 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,998 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further perform extensive experiments to demonstrate the effectiveness across various settings. Overall, our procedure offers a novel path for practitioners to make use of potentially high-dimensional instruments (e.g., as in Mendelian randomization). |
| Researcher Affiliation | Academia | 1 LMU Munich 2 Munich Center for Machine Learning (MCML) 3 School of Computation, Information and Technology, TU Munich 4 Helmholtz Munich. Correspondence to: Jonas Schweisthal <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Two-stage learner for estimating bounds with complex instruments |
| Open Source Code | Yes | 2Code is available at https://github.com/JSchweisthal/ComplexPartialIdentif. |
| Open Datasets | Yes | We provide results using real-world data from an ADJUVANT chemotherapy study (Liu et al., 2021) as provided in https://github.com/cancer-oncogenomics/minerva-adjuvant-nsclc/tree/v1.0.0. |
| Dataset Splits | Yes | To create the simulated data used in Sec. 6, we sample n = 2000 from the data-generating process above. We then split the data into train (40%), val (20%), and test (40%) sets such that the bounds and deviation can be calculated on the same amount of data for training and testing. |
| Hardware Specification | Yes | Each training run of the experiments could be performed on a CPU with 8 cores in under 15 minutes. |
| Software Dependencies | No | We use PyTorch Lightning for implementation. Each training run of the experiments could be performed on a CPU with 8 cores in under 15 minutes. |
| Experiment Setup | Yes | For all models, we use the Adam optimizer with a learning rate of 0.03. We train our models for a maximum of 100 epochs and apply early stopping. For our method, we fixed λ = 1 and performed random search to tune for [0, 1] for γ. |