Adjustment Criteria for Generalizing Experimental Findings
Authors: Juan Correa, Jin Tian, Elias Bareinboim
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we investigate the assumptions and machinery necessary for using covariate adjustment to correct for the biases generated by both of these problems, and generalize experimental data to infer causal effects in a new domain. We derive complete graphical conditions to determine if a set of covariates is admissible for adjustment in this new setting. Building on the graphical characterization, we develop an efficient algorithm that enumerates all possible admissible sets with polytime delay guarantee; this can be useful for when some variables are preferred over the others due to different costs or amenability to measurement. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Purdue University, Indiana, USA 2Computer Science Department, Iowa State University, IA, USA. Correspondence to: Juan D. Correa <correagr@purdue.edu>. |
| Pseudocode | Yes | Algorithm 1 Is EAdmissible(G, X, Y, Z) Algorithm 2 List GAdj Sets(D, X, Y, W) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. Therefore, no information about publicly available or open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would require train/validation/test dataset splits. No specific splitting information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. No specific hardware details are mentioned. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers that would be required to replicate the theoretical derivations or algorithms in a computational environment. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments involving a specific setup, hyperparameters, or training configurations. |