Partial Counterfactual Identification from Observational and Experimental Data
Authors: Junzhe Zhang, Jin Tian, Elias Bareinboim
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithms are validated extensively on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Columbia University 2Department of Computer Science, Iowa State University. |
| Pseudocode | Yes | Algorithm 1 CREDIBLEINTERVAL |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | International stroke trials was a large, randomized, open trial of up to 14 days of antithrombotic therapy after stroke onset (Carolei et al., 1997). ... We collect N = 10^3 synthetic observational samples v = {x(n), y(n)}N n=1 that are compatible with the Bow diagram of Fig. 1d. |
| Dataset Splits | No | The paper mentions collecting 'N = 10^4 observational samples' or 'N = 10^3 observational samples' for different experiments, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies, libraries, or solvers with version numbers that would be needed to replicate the experiments. |
| Experiment Setup | Yes | In all experiments, we evaluate our proposed strategy using credible intervals (ci). We draw at least 4 x 10^3 samples from the posterior distribution P (θctf | v) over the target counterfactual. This allows us to compute 100% credible interval over θctf within error ϵ = 0.05, with probability at least 1 δ = 0.95. |