Scalable Computation of Causal Bounds
Authors: Madhumitha Shridharan, Garud Iyengar
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark the greedy heuristic by computing bounds for 100 instances of each of the examples in Appendix A which do not satisfy the conditions in Section 3.2. Table 2 reports the results for Examples A, B and C for which the LP can be solved. We see that bounds from the greedy heuristic matches the LP bounds in most instances for these problems. Recall that one can compute the optimal bounds for Example A only after pruning the LP. In Table 2, ϵL = 1 αG L αL and ϵU = αG U αU 1 denote the relative errors of the lower and upper bounds, respectively. We see that the lower bound is always within 10% of the true value, whereas the upper bound is within 10% for at least 86% of the cases. |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering and Operations Research, Columbia University, New York, USA. Correspondence to: Madhumitha Shridharan <ms6143@columbia.edu>, Garud Iyengar <garud@ieor.columbia.edu>. |
| Pseudocode | Yes | Algorithm 1 Greedy Heuristic |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include links to a code repository. |
| Open Datasets | No | The paper describes a |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (e.g., percentages, sample counts, or cross-validation setup) needed to reproduce the experiment. |
| 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, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper describes data generating processes but does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations. |