Causal Discovery from Biomedical Data – June 8-11, 2015, Carnegie Mellon University, Pittsburgh, PA
Location: Baker Hall, Giant Eagle Auditorium, A51, CMU

Course Director: Richard Scheines, PhD

Day 1 Morning Presentation

Day 1 Afternoon Presentation

Day 2 Morning Presentation

Day 2 Afternoon Presentation

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Causal Graphical Models – Richard Scheines with Joe Ramsey

  • Introduction
    • Overview of causal graphical models
    • Loading Tetrad
    • Causal graphs/interventions
  • Building Models
    • Parametric models: Bayes nets, Structural Equation Models (SEM), etc.
    • Instantiated models
  • Estimation
    • Estimation
    • Inference
    • Updating
  • Hands-on Real-Data Examples

Dinner on your own, though CCD personnel will be on hand to give advice and rides.

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Search – Richard Scheines with Joe Ramsey

  • D-Separation
  • Model Equivalence
  • Search Basics
    • PC
    • GES
    • GES-IDA
    • GES Speed-up
  • Hands-on Real-Data Examples
  • Latent Variable Model Search
    • FCI
    • SEM search

Break early for Poster Session and Dinner (6-8 pm)

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Biomedical Causal Discovery Overview

  • Overview of biomedical problems as case studies
  • Using Background Knowledge
  • Time Series
  • Handling Feedback
  • Unit of Analysis (single unit, neurons, etc.)

Dinner on your own, though CCD personnel will be on hand to give advice and rides.

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Biomedical Causal Discovery Case Studies

Attendees choose the biomedical case studies of most interest to them

  • Morning case studies
  • Group Discussion
    • Overview by CCD Systems Architecture & Algorithm groups
    • Question & answer period
    • Interactive discussion of course material
    • Attendees discuss the types of datasets they need to model – help guide CCD algorithm development
    • CCD user outreach activities

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