Identifying Causal-Effect Inference Failure with
Uncertainty-Aware Models
Andrew Jesson
∗
Department of Computer Science
University of Oxford
Oxford, UK OX1 3QD
Sören Mindermann
*
Department of Computer Science
University of Oxford
Oxford, UK OX1 3QD
Uri Shalit
Technion
Haifa, Israel 3200003
Yarin Gal
Department of Computer Science
University of Oxford
Oxford, UK OX1 3QD
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 the train and test
distributions differ, 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 decision makers
when predictions are not to be trusted while outperforming standard methods that
use the propensity score to identify lack of overlap.
1 Introduction
Learning individual-level causal effects is concerned with learning how units of interest respond
to interventions or treatments. These could be the medications prescribed to particular patients,
training-programs to job seekers, or educational courses for students. Ideally, such causal effects
would be estimated from randomized controlled trials, but in many cases, such trials are unethical
or expensive: researchers cannot randomly prescribe smoking to assess health risks. Observational
data offers an alternative, with typically larger sample sizes and lower costs, and more relevance
to the target population. However, the price we pay for using observational data is lower certainty
in our causal estimates, due to the possibility of unmeasured confounding, and the measured and
unmeasured differences between the populations who were subject to different treatments.
Progress in learning individual-level causal effects is being accelerated by deep learning approaches
to causal inference [
27
,
36
,
3
,
48
]. Such neural networks can be used to learn causal effects from
∗
Equal contribution.
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada.
arXiv:2007.00163v2 [cs.LG] 22 Oct 2020
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