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 [1].
Transportability from Multiple Environments with Limited Experiments: Completeness Results
Authors: Elias Bareinboim, Judea Pearl
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper addresses the problem of mz-transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental data can be collected. The paper first establishes a necessary and sufficient condition for deciding the feasibility of mz-transportability, i.e., whether causal effects in the target domain are estimable from the information available. It further proves that a previously established algorithm for computing transport formula is in fact complete, that is, failure of the algorithm implies non-existence of a transport formula. Finally, the paper shows that the do-calculus is complete for the mz-transportability class. |
| Researcher Affiliation | Academia | Elias Bareinboim Computer Science UCLA EMAIL Judea Pearl Computer Science UCLA EMAIL |
| Pseudocode | Yes | Figure 3: Modified version of identification algorithm capable of recognizing mz-transportability. PROCEDURE TRmz(y, x, P, I, S, W, D) |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and focuses on completeness results and mathematical proofs. It does not describe empirical experiments using datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would involve validation data splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |