Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Estimating individual treatment effects under unobserved confounding using binary instruments
Authors: Dennis Frauen, Stefan Feuerriegel
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 35,862 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,726 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 | Across various computational experiments, we demonstrate empirically that our MRIV-Net achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Dennis Frauen1, 2 & Stefan Feuerriegel1, 2 1 Munich Center for Machine Learning 2 LMU Munich |
| Pseudocode | Yes | Algorithm 1: MRIV |
| Open Source Code | Yes | Reproducibility: The codes for reproducing the experimental results can be found at https: //github.com/Dennis Frauen/MRIV-Net. |
| Open Datasets | Yes | The so-called Oregon health insurance experiment (OHIE) (Finkelstein et al., 2012) was an important RCT with non-compliance. [...] Data available here: https://www.nber.org/programs-projects/projects-and-centers/oregon-health-insurance-experiment |
| Dataset Splits | Yes | For all methods except KIV and DFIV, we split the data into a training set (80%) and a validation set (20%). |
| Hardware Specification | Yes | For training, we used an AMD Ryzen Pro 7 CPU. |
| Software Dependencies | No | The paper mentions using the 'scikit-learn package' and 'R package Biased Urn' but does not provide specific version numbers for these or any other software dependencies such as deep learning frameworks (e.g., PyTorch, TensorFlow) or Python. |
| Experiment Setup | Yes | We use two hidden layers with RELU activation functions. We also incorporated a dropout layer for each hidden layer. We trained all models with the Adam optimizer (Kingma & Ba, 2015) using 100 epochs. [...] The tuning ranges for the hyperparameter are shown in Table 6. |