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Found 8 papers, 3 papers with code
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
Building on the literature of instrumental variables (IVs), a plethora of methods has been developed to identify causal effects in linear systems.
A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning Algorithms
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 dataset shifts and can be used for prediction in new, unseen environments.
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
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.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
In this paper, we provide an algorithm for the identification of causal parameters in linear structural models that subsumes previous state-of-the-art methods.
Identification and Overidentification of Linear Structural Equation Models
In this paper, we address the problems of identifying linear structural equation models and discovering the constraints they imply.
Incorporating Knowledge into Structural Equation Models using Auxiliary Variables
This cancellation allows the auxiliary variables to help conventional methods of identification (e. g., single-door criterion, instrumental variables, half-trek criterion), as well as model testing (e. g., d-separation, over-identification).
Decomposition and Identification of Linear Structural Equation Models
In this paper, we address the problem of identifying linear structural equation models.
