DeepAI AI Chat
Log In Sign Up

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction

10/28/2018
by   William Herlands, et al.
NYU college
Parallels IP Holdings GmbH
Carnegie Mellon University
cornell university
8

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.

READ FULL TEXT

page 8

page 26

page 27

page 28

page 30

page 32

page 35

page 37

11/13/2015

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

We present a scalable Gaussian process model for identifying and charact...
10/06/2017

Machine Learning for Drug Overdose Surveillance

We describe two recently proposed machine learning approaches for discov...
09/13/2021

DisCERN:Discovering Counterfactual Explanations using Relevance Features from Neighbourhoods

Counterfactual explanations focus on "actionable knowledge" to help end-...
04/27/2019

An R Package for Spatio-Temporal Change of Support

Spatio-temporal change of support (STCOS) methods are designed for stati...
10/21/2019

Towards User Empowerment

Counterfactual explanations can be obtained by identifying the smallest ...
05/31/2021

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests

Informally, a `spurious correlation' is the dependence of a model on som...
05/31/2021

Online Bayesian inference for multiple changepoints and risk assessment

The aim of the present study is to detect abrupt trend changes in the me...