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Found 8 papers, 4 papers with code
The PetShop Dataset -- Finding Causes of Performance Issues across Microservices
Although the machine learning and systems research communities have proposed various techniques to tackle this problem, there is currently a lack of standardized datasets for quantitative benchmarking.
Toward Falsifying Causal Graphs Using a Permutation-Based Test
By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random.
Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Statistical prediction models are often trained on data from different probability distributions than their eventual use cases.
Causal Forecasting:Generalization Bounds for Autoregressive Models
Under causal sufficiency, the problem of causal generalization amounts to learning under covariate shifts, albeit with additional structure (restriction to interventional distributions under the VAR model).
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
We present Amazon SageMaker Clarify, an explainability feature for Amazon SageMaker that launched in December 2020, providing insights into data and ML models by identifying biases and explaining predictions.
Linear Dynamics: Clustering without identification
Linear dynamical systems are a fundamental and powerful parametric model class.
Explaining an increase in predicted risk for clinical alerts
We consider explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
