4th Workshop on Data Mining for Medical Informatics:
Causal Inference for
Health Data Analytics

Nov 4, 2017, Washington, DC

To be held in conjunction with the AMIA 2017 Annual Symposium

DMMI 2017 workshop is sponsored by AMIA Knowledge Discovery and Data Mining Working Group 

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).

Topics and Scope

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.
  1. Paper submissions must be no more than six pages in length, inclusive of figures and references. 
  2. 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.

Full papers and abstracts must be submitted electronically through the EasyChair system at this link:

Selected submissions will be invited to the Journal of Health Informatics Research, the journal website is here.

Important Dates

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






Kenney Ng
IBM Research
Bisakha Ray
New York University
SiSi Ma
University of Minnesota
Kun Zhang
Carnegie Mellon University
Fei Wang
Cornell University

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 Schedule


Invited Speakers



Prof. Constantin Aliferis
Prof. Gregory Cooper
Prof. David Danks
Prof. Miguel Hernan