Bryant Chen
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View article: A unifying causal framework for analyzing dataset shift-stable learning algorithms
A unifying causal framework for analyzing dataset shift-stable learning algorithms Open
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 finding predictive distributions that are invariant to datas…
View article: A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models
A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models Open
Modern causal analysis involves two major tasks, discovery and identification. The first aims to learn a causal structure compatible with the available data, the second leverages that structure to estimate causal effects. Rather than perfo…
View article: Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets Open
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 identifiable from a combination of observational data and …
View article: Should I Include this Edge in my Prediction? Analyzing the Stability-Performance Tradeoff
Should I Include this Edge in my Prediction? Analyzing the Stability-Performance Tradeoff Open
Recent work addressing model reliability and generalization has resulted in a variety of methods that seek to proactively address differences between the training and unknown target environments. While most methods achieve this by finding …
View article: Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering Open
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on dat…
View article: Detecting Backdoor Attacks on Deep Neural Networks by Activation\n Clustering
Detecting Backdoor Attacks on Deep Neural Networks by Activation\n Clustering Open
While machine learning (ML) models are being increasingly trusted to make\ndecisions in different and varying areas, the safety of systems using such\nmodels has become an increasing concern. In particular, ML models are often\ntrained on …
View article: Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables Open
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 family of methods known as instrumental variables. The identi…
View article: Identification by Edge Contraction in Linear Structural Equation Models
Identification by Edge Contraction in Linear Structural Equation Models Open
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 example, from a previously conducted randomized experiment, from…
View article: Incorporating Knowledge into Structural Equation Models using Auxiliary\n Variables
Incorporating Knowledge into Structural Equation Models using Auxiliary\n Variables Open
In this paper, we extend graph-based identification methods by allowing\nbackground knowledge in the form of non-zero parameter values. Such information\ncould be obtained, for example, from a previously conducted randomized\nexperiment, f…
View article: Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
Incorporating Knowledge into Structural Equation Models using Auxiliary Variables Open
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 example, from a previously conducted randomized experiment, from…
View article: Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models.
Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models. Open
We extend graph-based identification methods for linear models by allowing background knowledge in the form of externally evaluated parameters. Such information could be obtained, for example, from a previously conducted randomized experim…
View article: Decomposition and Identification of Linear Structural Equation Models
Decomposition and Identification of Linear Structural Equation Models Open
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 show that any semi-Markovian linear model can be recursive…