Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models

Abstract

Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical domains such as healthcare, where estimating and communicating uncertainty to decision-makers is crucial. We introduce a practical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applications of causal effect approaches fail. Further, our methods allow us to handle covariate shift, where test distribution differs to train distribution, common when systems are deployed in practice. We show that when such a covariate shift occurs, correctly modeling uncertainty can keep us from giving overconfident and potentially harmful recommendations. We demonstrate our methodology with a range of state-of-the-art models. Under both covariate shift and lack of overlap, our uncertainty-equipped methods can alert decisions makers when predictions are not to be trusted while outperforming their uncertainty-oblivious counterparts.

References in corpus (10)

• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Bayesian Active Learning for Classification and Preference Learning
• Deep Anomaly Detection with Outlier Exposure
• Causal Effect Inference with Deep Latent-Variable Models
• Functional Variational Bayesian Neural Networks
• Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes
• Bayesian Layers: A Module for Neural Network Uncertainty
• A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
• Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
• Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

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