Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
This provides a more expressive framework for identifying parameter changes in machine learning and statistics, with applications in scientific and policy domains, though it is incremental as it builds on existing changepoint models.
The authors tackled the problem of limited expressiveness in changepoint models by introducing change surfaces as a multidimensional generalization, enabling variable and non-monotonic changes, and demonstrated its application with Gaussian Process Change Surfaces to discover complex changes in spatio-temporal datasets, such as heterogeneous measles incidence changes after vaccine introduction and spatial-demographic patterns in lead testing requests.
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalization of changepoints. We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions. Additionally, we show how change surfaces can be used for counterfactual prediction. As a concrete instantiation of the change surface framework, we develop Gaussian Process Change Surfaces (GPCS). We demonstrate counterfactual prediction with Bayesian posterior mean and credible sets, as well as massive scalability by introducing novel methods for additive non-separable kernels. Using two large spatio-temporal datasets we employ GPCS to discover and characterize complex changes that can provide scientific and policy relevant insights. Specifically, we analyze twentieth century measles incidence across the United States and discover previously unknown heterogeneous changes after the introduction of the measles vaccine. Additionally, we apply the model to requests for lead testing kits in New York City, discovering distinct spatial and demographic patterns.