4th Workshop on Data Mining for Medical Informatics: Causal Inference for Health Data Analytics
Nov 4, 2017, Washington, DC
The biomedical sciences and healthcare
are contributing significantly to the big data revolution through advances in
genomic sequencing technology and imaging, clinical and personally-generated
data. Data mining and machine learning techniques have played an increasingly
important role in medical informatics with the goal of discovering knowledge
and insights from various data sources. Causal inference is an important
methodological pool from which one can draw powerful techniques for knowledge
discovery and data-driven insights. Causal discovery methods were developed to
address the financial and ethical concerns associated with randomized
controlled trials. An attestation to their significance is that they have been
recognized with the Turing Award in computer science and the Nobel Prize in
economics, and have led to exciting interdisciplinary research in statistics,
philosophy, social sciences, and neuroscience. Discovery of causality is a
major goal in basic, translational and clinical science. In computational
biology, neuroscience, epidemiology and biomedicine one often faces the
daunting task of finding causal relationships in very large-dimensional data. This
highlights the necessity to develop and evaluate algorithms and tools to
improve the current state of the art in causal discovery from experimental, quasi-experimental
and non-experimental (i.e., observational) data. The main theme of the workshop this year is causal inference for health data
analytics, which aims to address both the
theoretical and experimental underpinnings of these methods. This includes
development and applications of the methods and discussions on how to make them
practically useful to clinicians, patients and other healthcare stakeholders. This topic is timely and has received a lot of interest
recently. We would like to invite researchers from both academia and industry
who are interested in this topic to participate in this workshop, share their opinions
and experience, as well as discuss future directions. This year's DMMI workshop will be co-located with the 2017 American Medical Informatics Association (AMIA) Annual Symposium. For more information on the prior DMMI Workshops click here (2014, Washington, DC), here (2015, San Francisco, CA), or here (2016, Chicago, IL).
Topic areas for the workshop include (but are not limited to) the following: - Research design for causal inference in
real-world data
- Causal structure discovery in large-scale,
observational data
- Intersection of machine learning and causal
inference
- Real-world
medical and health applications of causal analysis
- Causal
inference from personally-generated data and surrogate data sources
- Causal
inference software and tools
Paper Submission and Format Guidelines
We encourage a diverse range of submissions and demonstrations from academic, healthcare organizations, and industry that addresses any of the topics listed above. Submissions can be for (1) paper / podium presentations, or (2) abstract / podium presentations.
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Paper submissions must be no more than six pages in length, inclusive of figures and references.
- Abstract submissions are limited to two pages.
Papers should be formatted in AMIA format styles. Manuscripts must be submitted as Adobe Portable Document Format (PDF) files. Other file formats will not be accepted.
Selected submissions will be invited to the Journal of Health Informatics Research, the journal website is here.
Deadline for submission: |
August 31, 2017 |
Notification of acceptance: |
September 15, 2017 |
Camera-ready Papers Due: |
October 20, 2017 |
Workshop: |
November 4, 2017 |
Workshop Organizing Committee
Program Committee
- Zach Shahn, IBM Research
- Cao Xiao, IBM Research
- Erich Kummerfeld, University of Minnesota
- Chih-Lin Chi, University of Minnesota
- Narges Razavian, NYU School of Medicine
- Himanshu Grover, NYU School of Medicine
Workshop Location- Date/Time: 8:30 AM - 4:30 PM, November 4, 2017
- Location: International Ballroom East, Washington Hilton (floorplan)
- Links:
Workshop Schedule
W07: Data Mining for Medical Informatics (DMMI) – Causal Inference for
Health Data Analytics (sponsored by the Knowledge Discovery and
Data Mining Working Group)
Invited Speakers
Speaker | Topic | Abstract | Prof. Miguel Hernan | An algorithm for causal inference from observational data | Making decisions among several courses of action requires knowledge about the causal effects of each action. Randomized experiments are the preferred method to quantify those causal effects. When randomized experiments are not feasible or available, causal effects are estimated from non-experimental or observational databases. Therefore, causal inference from observational databases can be viewed as an attempt to emulate a hypothetical randomized experiment—the target experiment or target trial—that would quantify the causal effect of interest. This talk outlines a general algorithm for causal inference using observational databases that makes the target trial explicit. This causal framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational analyses, and helps avoid common methodologic pitfalls.
| Prof. David Danks | Causal discovery from time series data: From theory to application | Many biomedical and scientific investigations aim to understand a system that dynamically changes over time, such as neural processes in the brain, development of cancerous tissues, or health trajectories for diverse patients. Moreover, we frequently need to learn causal models of the underlying dynamical systems, as we want to not only predict their behavior, but also design effective interventions--actions, policies, modifications, and so forth--to control them and achieve desired health outcomes. In this talk, I will discuss different strategies for causal discovery from dynamical or time series data, with a particular focus on learning from complex types of data or datasets. There are important considerations that must be resolved in advance of the causal discovery process, but these can be encapsulated in a few key questions. Finally, I will provide some biomedical examples of causal discovery from time series data.
| Prof. Constantin Aliferis
| Causal Feature Selection | Causality is not only important for designing interventions that will steer a system of interest to a desired state, but also has a central importance for feature selection for predictive modeling. This talk will discuss Markov Boundary inference as a solution to the vanilla feature selection problem. We will first describe theoretical foundations, then describe algorithmic approaches and finally we will examine empirical results from a variety of domains that test how well theoretical expectations are reflected in real-world data analysis. We will also contrast causal with non-causal feature selection both theoretically and empirically.
| Prof. Gregory Cooper
| Graphical Causal Discovery from Big Biomedical Data | Science is centrally concerned with the discovery of causal
relationships in nature. In the past 25 years there has been tremendous progress
in the development of graphical methods for representing and discovering causal
relationships from data, including big biomedical data. The Center for Causal
Discovery (CCD) is developing and making available state-of-the-art graphical
causal discovery software that is capable of analyzing very large biomedical
datasets. This talk will present a brief overview of the CCD, an introduction
to graphical causal discovery methods, and several examples of the use of these
methods in analyzing biomedical data.
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