The Hierarchy of Stable Distributions and Operators to Trade Off Stability and Performance

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 distributions that will be invariant across environments, we will show they do not necessarily find the same distributions which has implications for performance. In this paper we unify existing work on prediction using stable distributions by relating environmental shifts to edges in the graph underlying a prediction problem, and characterize stable distributions as those which effectively remove these edges. We then quantify the effect of edge deletion on performance in the linear case and corroborate the findings in a simulated and real data experiment

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens

After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity. Although prior work has shown how to validate models in the presence of performativity using causal inference techniques, there has been little work on how to monitor models in the presence of performativity. Unlike the setting of model validation, there is much less agreement on which performance metrics to monitor. Different monitoring criteria impact how interpretable the resulting test statistic is, what assumptions are needed for identifiability, and the speed of detection. When this choice is further coupled with the decision to use observational versus interventional data, ML deployment teams are faced with a multitude of monitoring options. The aim of this work is to highlight the relatively under-appreciated complexity of designing a monitoring strategy and how causal reasoning can provide a systematic framework for choosing between these options. As a motivating example, we consider an ML-based risk prediction algorithm for predicting unplanned readmissions. Bringing together tools from causal inference and statistical process control, we consider six monitoring procedures (three candidate monitoring criteria and two data sources) and investigate their operating characteristics in simulation studies. Results from this case study emphasize the seemingly simple (and obvious) fact that not all monitoring systems are created equal, which has real-world impacts on the design and documentation of ML monitoring systems

The Stability and Accuracy Tradeoff Under Dataset Shift: A Causal Graphical Analysis

Recent interest in dataset shift has produced many methods for finding invariant distributions for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical representation to express various types of dataset shifts. We show that all invariant distributions correspond to a causal hierarchy of graphical operators which disable the edges in the graph that are responsible for the shifts. The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. We use it to establish conditions for minimax optimal performance across environments, and derive new algorithms that find optimal stable distributions. Using this new perspective, we empirically demonstrate that that there is a tradeoff between minimax and average performance