Causal Inference in Observational Data Article Swipe

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Pranjul Yadav , Lisiane Prunelli , Alexander Hoff , Michael Steinbach , Bonnie L. Westra , Vipin Kumar , György Simon · YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1611.04660

Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/1611.04660

PDF

https://arxiv.org/pdf/1611.04660

OA Status

green

Cited By

1

Related Works

10

OpenAlex ID

https://openalex.org/W4298272844

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OpenAlex ID

https://openalex.org/W4298272844

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DOI

https://doi.org/10.48550/arxiv.1611.04660

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Title

Causal Inference in Observational Data

Work title

Type

preprint

OpenAlex work type

Language

en

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Publication year

2016

Year of publication

Publication date

2016-11-15

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Authors

Pranjul Yadav, Lisiane Prunelli, Alexander Hoff, Michael Steinbach, Bonnie L. Westra, Vipin Kumar, György Simon

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Landing page

https://arxiv.org/abs/1611.04660

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PDF URL

https://arxiv.org/pdf/1611.04660

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Open access

Yes

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OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/1611.04660

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Concepts

Causal inference, Observational study, Intervention (counseling), Set (abstract data type), Psychological intervention, Association rule learning, Medicine, Computer science, Inference, Population, Machine learning, Artificial intelligence, Data mining, Data science, Psychiatry, Environmental health, Programming language, Pathology

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Cited by

1

Total citation count in OpenAlex

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2025: 1

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Related works (count)

10

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