Generalized Instrumental Variables

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 (13)

• Drawing and Analyzing Causal DAGs with DAGitty
• Quantifying the Causal Effects of Conversational Tendencies
• Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
• 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
• Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past
• Interpretable Almost-Matching-Exactly With Instrumental Variables
• Determinantal Generalizations of Instrumental Variables
• Bayesian Inference for Gaussian Mixed Graph Models
• Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing
• Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
• The Graphical Identification for Total Effects by using Surrogate Variables