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Full-Text Articles in Artificial Intelligence and Robotics
Combinatorial Algorithms For Graph Discovery And Experimental Design, Raghavendra K. Addanki
Combinatorial Algorithms For Graph Discovery And Experimental Design, Raghavendra K. Addanki
Doctoral Dissertations
In this thesis, we study the design and analysis of algorithms for discovering the structure and properties of an unknown graph, with applications in two different domains: causal inference and sublinear graph algorithms. In both these domains, graph discovery is possible using restricted forms of experiments, and our objective is to design low-cost experiments. First, we describe efficient experimental approaches to the causal discovery problem, which in its simplest form, asks us to identify the causal relations (edges of the unknown graph) between variables (vertices of the unknown graph) of a given system. For causal discovery, we study algorithms …
Testability Of Instrumental Variables In Linear Non-Gaussian Acyclic Causal Models, Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang
Testability Of Instrumental Variables In Linear Non-Gaussian Acyclic Causal Models, Feng Xie, Yangbo He, Zhi Geng, Zhengming Chen, Ru Hou, Kun Zhang
Machine Learning Faculty Publications
This paper investigates the problem of selecting instrumental variables relative to a target causal influence X → Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria …