Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Nagpal, Chirag; Wei, Dennis; Vinzamuri, Bhanukiran; Shekhar, Monica; Berger, Sara; Das, Subhro; Varshney, Kush R.

doi:10.1145/3368555.3384456

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Such identification is often referred to as subgroup discovery or subgroup analysis. While there has been extensive study in subgroup recovery with binary or continuous outcomes (Lipkovich et al, 2011;Foster et al, 2011;Nagpal et al, 2020;Wang and Rudin, 2022), censored time-to-event outcomes are less studied in the context of phenotyping.…”

Section: Counterfactual Phenotypingmentioning

confidence: 99%

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

Nagpal¹,

Potosnak²,

Dubrawski³

2022

Preprint

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Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes. In this paper, we present auton-survival, an open-source repository of tools to streamline working with censored time-to-event or survival data. auton-survival includes tools for survival regression, adjustment in the presence of domain shift, counterfactual estimation, phenotyping for risk stratification, evaluation, as well as estimation of treatment effects. Through real world case studies employing a large subset of the SEER oncology incidence data, we demonstrate the ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions.

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Section: Counterfactual Phenotypingmentioning

confidence: 99%

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

Nagpal¹,

Potosnak²,

Dubrawski³

2022

Preprint

Self Cite

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“…The causal rule sets method finds short rules that do not necessarily have a tree structure (Wang and Rudin, 2022 ). The heterogeneous effect mixture model (HEMM) uses mixture distributions rather than crisp rules (Nagpal et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Targeting resources efficiently and justifiably by combining causal machine learning and theory

Ali

2022

Front. Artif. Intell.

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IntroductionEfficient allocation of limited resources relies on accurate estimates of potential incremental benefits for each candidate. These heterogeneous treatment effects (HTE) can be estimated with properly specified theory-driven models and observational data that contain all confounders. Using causal machine learning to estimate HTE from big data offers higher benefits with limited resources by identifying additional heterogeneity dimensions and fitting arbitrary functional forms and interactions, but decisions based on black-box models are not justifiable.MethodsOur solution is designed to increase resource allocation efficiency, enhance the understanding of the treatment effects, and increase the acceptance of the resulting decisions with a rationale that is in line with existing theory. The case study identifies the right individuals to incentivize for increasing their physical activity to maximize the population's health benefits due to reduced diabetes and heart disease prevalence. We leverage large-scale data from multi-wave nationally representative health surveys and theory from the published global meta-analysis results. We train causal machine learning ensembles, extract the heterogeneity dimensions of the treatment effect, sign, and monotonicity of its moderators with explainable AI, and incorporate them into the theory-driven model with our generalized linear model with the qualitative constraint (GLM_QC) method.ResultsThe results show that the proposed methodology improves the expected health benefits for diabetes by 11% and for heart disease by 9% compared to the traditional approach of using the model specification from the literature and estimating the model with large-scale data. Qualitative constraints not only prevent counter-intuitive effects but also improve achieved benefits by regularizing the model.

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“…Networks. Nagpal et al (2020) explore the question of which types of prescription opioids (e.g., natural, semi-synthetic, synthetic) (T ) are most likely to cause long term addiction (Y ). Because of predisposition to different injuries, type of employment (X) could be a common cause of both treatment and outcome.…”

Section: Why Use Deep Learning For Causal Inference?mentioning

confidence: 99%

Deep Learning for Causal Inference

Koch¹,

Sainburg²,

Geraldo³

et al. 2021

Preprint

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This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

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Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines

Cited by 15 publications

References 38 publications

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

Targeting resources efficiently and justifiably by combining causal machine learning and theory

Deep Learning for Causal Inference

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