Causes of Effects: Learning Individual Responses from Population Data
Authors: Scott Mueller, Ang Li, Judea Pearl
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Simulation Results We randomly generated 100000 sample distributions compatible with each the causal diagrams depicted in Figures 4a, 1a, 4b, and 3 for Theorems 4, 5, 6, and 7, respectively. Given sample distribution i, let [ai, bi] be the bounds utilizing the proposed Theorems and [ci, di] be the traditional Tian-Pearl bounds [Li and Pearl, 2021]. The following is computed for each causal diagram as summarized in Table 2: |
| Researcher Affiliation | Academia | Scott Mueller , Ang Li and Judea Pearl Cognitive Systems Laboratory, Computer Science Department, University of California Los Angeles {scott, angli, judea}@cs.ucla.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to supplementary material (https://ftp.cs.ucla.edu/ pub/stat ser/r505-sup.pdf), but it does not explicitly state that this link contains the open-source code for the methodology described in the paper. |
| Open Datasets | No | The paper uses 'randomly generated sample distributions' for simulations and observational data from 'a drug study' and 'RCT data' for examples, but does not provide concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes simulation results from randomly generated distributions but does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) related to a model's training. |