General Transportability of Soft Interventions: Completeness Results
Authors: Juan Correa, Elias Bareinboim
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we extend transportability theory to encompass these more complex types of interventions, which are known as soft, both relative to the input as well as the target distribution of the analysis. Specifically, we develop a graphical condition that is both necessary and sufficient for deciding soft-transportability. Second, we develop an algorithm to determine whether a non-atomic intervention is computable from a combination of the distributions available across domains. As a corollary, we show that the σ-calculus is complete for the task of soft-transportability. |
| Researcher Affiliation | Academia | Juan D. Correa Elias Bareinboim Causal Artificial Intelligence Laboratory Columbia University {jdcorrea,eb}@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1 σ-TR(Y, W, σX, Z, G ) |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or provide access information for any specific public datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not report on empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe the specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers needed to replicate any experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameters or training configurations. |