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Roy Adams

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3 papers

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UAI Conference 2021 Conference Paper

Partial Identifiability in Discrete Data with Measurement Error

Noam Finkelstein
Roy Adams
Suchi Saria
Ilya Shpitser

When data contains measurement errors, it is necessary to make modeling assumptions relating the error-prone measurements to the unobserved true values. Work on measurement error has largely focused on models that fully identify the parameter of interest. As a result, many practically useful models that result in bounds on the target parameter – known as partial identification – have been neglected. In this work, we present a method for partial identification in a class of measurement error models involving discrete variables. We focus on models that impose linear constraints on the target parameter, allowing us to compute partial identification bounds using off-the-shelf LP solvers. We show how several common measurement error assumptions can be composed with an extended class of instrumental variable-type models to create such linear constraint sets. We further show how this approach can be used to bound causal parameters, such as the average treatment effect, when treatment or outcome variables are measured with error. Using data from the Oregon Health Insurance Experiment, we apply this method to estimate bounds on the effect Medicaid enrollment has on depression when depression is measured with error.

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ICML Conference 2019 Conference Paper

Learning Models from Data with Measurement Error: Tackling Underreporting

Roy Adams
Yuelong Ji
Xiaobin Wang
Suchi Saria

Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. In this paper we present a method for estimating the distribution of an outcome given a binary exposure that is subject to underreporting. Our method is based on a missing data view of the measurement error problem, where the true exposure is treated as a latent variable that is marginalized out of a joint model. We prove three different conditions under which the outcome distribution can still be identified from data containing only error-prone observations of the exposure. We demonstrate this method on synthetic data and analyze its sensitivity to near violations of the identifiability conditions. Finally, we use this method to estimate the effects of maternal smoking and heroin use during pregnancy on childhood obesity, two import problems from public health. Using the proposed method, we estimate these effects using only subject-reported drug use data and refine the range of estimates generated by a sensitivity analysis-based approach. Further, the estimates produced by our method are consistent with existing literature on both the effects of maternal smoking and the rate at which subjects underreport smoking.

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UAI Conference 2018 Conference Paper

Learning Time Series Segmentation Models from Temporally Imprecise Labels

Roy Adams
Benjamin M. Marlin

This paper considers the problem of learning time series segmentation models when the labeled data are subject to temporal uncertainty or noise. Our approach augments the semi- Markov conditional random field (semi-CRF) model with a probabilistic model of the label observation process. This augmentation allows us to estimate the parameters of the semi-CRF from timestamps corresponding roughly to the occurrence of transitions between segments. We show how exact marginal inference can be performed in the augmented model in polynomial time, enabling learning based on marginal likelihood maximization. Our experiments on two activity detection problems show that the proposed approach can learn models from temporally imprecise labels, and can successfully refine imprecise segmentations through posterior inference. Finally, we show how inference complexity can be reduced by a factor of 40 using static and model-based pruning of the inference dynamic program.

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