Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bounds on Causal Effects and Application to High Dimensional Data
Authors: Ang Li, Judea Pearl5773-5780
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | and demonstrate its performance using simulation studies. Herein, we present a simulated example to demonstrate that the midpoints of the bounds on the causal effects given by Theorem 4 are adequate for estimating the causal effects. |
| Researcher Affiliation | Academia | 1 Cognitive Systems Laboratory, Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA. EMAIL |
| Pseudocode | Yes | Algorithm 1: Generate Equivalent Tuple |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology, nor does it provide a link to a code repository for the work described. |
| Open Datasets | No | The paper describes generating sample distributions for simulation studies and presents observational data in tables, but it does not specify a publicly available or open dataset with concrete access information (e.g., link, DOI, formal citation). |
| Dataset Splits | No | The paper does not specify explicit training/test/validation dataset splits or provide details on cross-validation setups. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | Yes | With the help of the SLSQP solver (Kraft 1988) in the scipy package (Sci Py Community 2020), we obtain the bounds on the causal effect... |
| Experiment Setup | No | The paper describes generating data for simulations and uses an optimization solver, but it does not provide specific experimental setup details such as hyperparameters, model initialization, or training schedules. |