20 citations · 48 across the 5 of their papers we have counts for
5 papers
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Daniel Kumor, Bryant Chen, Elias Bareinboim
One of the most common mistakes made when performing data analysis is attributing causal meaning to regression coefficients. Formally, a causal effect can only be computed if it is…
A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning Algorithms
Adarsh Subbaswamy, Bryant Chen, Suchi Saria
Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for fi…
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
Bryant Chen, Daniel Kumor, Elias Bareinboim
We developed a novel approach to identification and model testing in linear structural equation models (SEMs) based on auxiliary variables (AVs), which generalizes a widely-used fa…
Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
Bryant Chen, Judea Pearl, Elias Bareinboim
In this paper, we extend graph-based identification methods by allowing background knowledge in the form of non-zero parameter values. Such information could be obtained, for examp…
Decomposition and Identification of Linear Structural Equation Models
Bryant Chen
In this paper, we address the problem of identifying linear structural equation models. We first extend the edge set half-trek criterion to cover a broader class of models. We then…