General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains
Authors: Sanghack Lee, Juan Correa, Elias Bareinboim10210-10217
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Specifically, we introduce a unified graphical criterion that characterizes the conditions under which conditional causal effects can be uniquely determined from the disparate data collections. We further develop an efficient, sound, and complete algorithm that outputs an expression for the conditional effect whenever it exists, which synthesizes the available causal knowledge and empirical evidence; if the algorithm is unable to find a formula, then such synthesis is provably impossible, unless further parametric assumptions are made. Finally, we prove that do-calculus (Pearl 1995) is complete for this task, i.e., the inexistence of a do-calculus derivation implies the impossibility of constructing the targeted causal explanation. |
| Researcher Affiliation | Academia | Sanghack Lee, Juan D. Correa, Elias Bareinboim Causal Artificial Intelligence Laboratory Columbia University, USA |
| Pseudocode | Yes | Algorithm 1 GTR and GTRU, sound and complete gtransportability algorithms. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on developing a framework and algorithm for causal inference. It discusses using "heterogeneous data collections" conceptually but does not refer to or provide access to specific training datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data splits. Therefore, it does not provide information on validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Consequently, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs. It does not mention any specific software dependencies with version numbers required to replicate empirical results. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup, thus no hyperparameter values or training configurations are provided. |