Bounds on Causal Effects and Application to High Dimensional Data
Authors: Ang Li, Judea Pearl5773-5780
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. {angli, judea}@cs.ucla.edu |
| 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. |