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.
A Neural Framework for Generalized Causal Sensitivity Analysis
Authors: Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 33,879 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,267 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 provide theoretical guarantees that NEURALCSA can infer valid bounds on the causal query of interest and also demonstrate this empirically using both simulated and real-world data. |
| Researcher Affiliation | Academia | 1 LMU Munich 2 Munich Center for Machine Learning 3 UCLA 4 University of Cambridge 5 Alan Turing Institute |
| Pseudocode | Yes | Algorithm 1: Full learning algorithm for NEURALCSA |
| Open Source Code | Yes | 7 Code is available at https://github.com/Dennis Frauen/Neural CSA. |
| Open Datasets | Yes | We create a semi-synthetic dataset using MIMIC-III (Johnson et al., 2016)... |
| Dataset Splits | Yes | We obtain n = 14719 patients and split the data into train (80%), val (10%), and test (10%). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software components like 'autoregressive neural spline flows' and 'Adam optimizer' but does not specify their version numbers or the version of the programming language used. |
| Experiment Setup | Yes | We choose the number of epochs such that NEURALCSA satisfies the sensitivity constraint for a given sensitivity parameter. Details are in Appendix F. ... Table 4: Hyperparameter tuning details. MODEL TUNABLE PARAMETERS SEARCH RANGE Stage 1 CNF Epochs 50 Batch size 32, 64, 128 Learning rate 0.0005, 0.001, 0.005 Hidden layer size (hyper network) 5, 10, 20, 30 Number of spline bins 2, 4, 8 Propensity network Epochs 30 Batch size 32, 64, 128 Learning rate 0.0005, 0.001, 0.005 Hidden layer size 5, 10, 20, 30 Dropout probability 0, 0.1 |