Generalized Instrumental Variables
arXiv:1301.0560
Abstract
This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Variables, which allows its application to models with few conditional independeces.
Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)
Cited by in corpus (5)
- On Identifying Total Effects in the Presence of Latent Variables and Selection bias
- A Criterion for Parameter Identification in Structural Equation Models
- Evaluation of the Causal Effect of Control Plans in Nonrecursive Structural Equation Models
- The Graphical Identification for Total Effects by using Surrogate Variables
- Bayesian Inference for Gaussian Mixed Graph Models