## Lecture: Preliminaries for Causal Networks
## Assigned Reading**Session Overview****Network Concept**- Read Chapter 20 in Statistical Analysis of Electronic Health Records, pages 487 to 497
- Read Chapter 1 in Causal Inference in Statistics: a Premier. Answers to Chapter Questions►
- What is a cause and how is it different from correlation Slides►
- Displaying a causal network Slides► Video► YouTube►
- Causal chain, common effect, and common cause in 3 variables Slides► YouTube► Video►
- Parents, children and cycles in networks Slides►
- Fidelity between network displays and formulas Slides► Video► YouTube►
## AssignmentInclude in the first page a summary page. In the summary page write statements comparing your work to answers given or videos. For example, "I got the same answers as the Teach One video for question 1." For these assignment you can use any statistical package, including R, SAS, and SPSS, Python. R packages and BNLearn are also used often. OpenBUGS and Gibbs Sampler, Stan, OpenMarkov, and Direct Graphical Model are also open source software. Netica is free for networks less than 15 nodes.
- Is the AI system effective for men?
- Is the AI system effective for women?
- Is the AI system effective across the population, if we do not know the gender of the person?
- Probability of being 18 to 29 years old
- Probability of being 30 to 40 years old given that you are at least 29 years old
- Expected value of age
- Name variables that precede Z
- Name variables that are not correlated with Z
- Name all of the parents of Z
- Name all of the ancestors of Z
- Name all of the children of W
- Name all of the descendants of W
- Identify all simple paths between X and T, where no node appears more than once
- Draw all directed paths between X and T
- What is the definition of a directed a-cyclical graph (DAG), and is this graph a DAG?
- What is the common cause of Y and Z?
Resources for Question 4: - Gelila Aboye's Teach One Slides► YouTube►
- Wang's Teach One, YouTube►
- Sully's Teach One, Slides►
- Sully's Python Code, see notes in Slides►
- Estimate p(High School)
- Estimate p(High School OR Female)
- Estimate p(High School | Female)
- Estimate p(Female| High School)
## MoreFor additional information (not part of the required reading), please see the following links: - Introduction to causal inference Read 1► Read 2► Video► Slides►
- Meta analysis through Bayesian networks Read►
- Introduction to Bayesian networks Read►
- Learning Bayesian Networks Read►
- Selection of Judea Pearl's articles PubMed►
- Applications of Bayesian networks in healthcare PubMed►
- Bayesian networks in neuroscience Read►
- Cost analysis using Bayesian networks Read►
- Bayesian network classifiers Read►
This page is part of the course on Comparative Effectiveness by Farrokh Alemi, Ph.D. Course Home► Email► |