Detecting and Measuring Confounding Using Causal Mechanism Shifts
Authors: Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results support the usefulness of the proposed measures. |
| Researcher Affiliation | Academia | Abbavaram Gowtham Reddy Indian Institute of Technology Hyderabad cs19resch11002@iith.ac.in Vineeth N Balasubramanian Indian Institute of Technology Hyderabad vineethnb@iith.ac.in |
| Pseudocode | Yes | Algorithm 1: Algorithm for evaluating pairwise CNF-1, CNF-2, CNF-3 |
| Open Source Code | Yes | Code to reproduce the results is presented in the supplementary material. Code is available at https://github.com/gautam0707/CD_CNF. |
| Open Datasets | No | To verify the performance of our method on a large scale, similar to [38], we generate causal graphs of various number nodes using Erdös-Rényi model. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits. |
| Hardware Specification | No | All the experiments are run on a CPU. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | In these experiments, each context is a result of intervention on one node. This is the reason for having the same value for number of nodes N and number of contexts |C|. Sample size denotes the number of data points used in each context. (Table 4 shows N, |C| values and Sample Sizes) |