Probabilities of Causation with Nonbinary Treatment and Effect
Authors: Ang Li, Judea Pearl
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we define and provide theoretical bounds for all types of probabilities of causation with multivalued treatments and effects. We further discuss examples where our bounds guide practical decisions and use simulation studies to evaluate how informative the bounds are for various data combinations. |
| Researcher Affiliation | Academia | Ang Li1, Judea Pearl2 1Department of Computer Science, Florida State University 2Cognitive Systems Laboratory, Department of Computer Science, University of California, Los Angeles angli@cs.fsu.edu, judea@cs.ucla.edu |
| Pseudocode | No | The paper mentions a 'generating algorithm' and refers to it being in the appendix, but the provided text does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the public release of its source code. |
| Open Datasets | No | The paper uses 'Experimental data' and 'Observational data' collected for specific examples (Tables 1-6) and mentions randomly 'generated samples' for simulation, but it does not refer to or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. The examples use observational and experimental data directly, and the simulation generates samples without discussing typical ML data splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the simulations or computations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the input data for its examples and the generation process for simulated data, but it does not provide details on experimental setup such as hyperparameters, model initialization, or specific training configurations typically found in experimental machine learning papers. |