Causal Effect Identifiability under Partial-Observability
Authors: Sanghack Lee, Elias Bareinboim
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the causal effect identifiability problem when the available distributions encompass different sets of variables, which we refer to as identification under partial-observability. We study a number of properties of the factors that comprise a causal effect under various levels of abstraction, and then characterize the relationship between them with respect to their status relative to the identification of a targeted intervention. We establish a sufficient graphical criterion for determining whether the effects are identifiable from partially-observed distributions. Finally, building on these graphical properties, we develop an algorithm that returns a formula for a causal effect in terms of the available distributions. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Columbia University, New York, NY 10027, USA. Correspondence to: Sanghack Lee <sanghacklee@cs.columbia.edu>. |
| Pseudocode | Yes | Algorithm 1 GID-PO |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the methodology described in the paper, nor does it provide a link to such code. |
| Open Datasets | No | The paper does not use or refer to any publicly available dataset for training or evaluation. Its content is theoretical, using illustrative causal graphs and distributions rather than empirical data. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits, as it focuses on theoretical development rather than empirical evaluation. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running experiments or computations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4) that would be needed to replicate any experimental setup. |
| Experiment Setup | No | The paper does not include details about an experimental setup, such as hyperparameter values, model initialization, or training schedules, as it presents theoretical work and an algorithm without empirical evaluation. |