Lecture: Causal Networks
Assigned Reading
AssignmentSubmit one file for all questions. Include all charts, code, and output in the same file. Start each question in a separate page or sheet. Include 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 this assignment you can use any statistical package, including R, SAS, and SPSS. Your instructor is familiar with Netica and BayesiaLab. R packages are also used often. OpenBUGS and Gibbs Sampler, Stan, OpenMarkov, and Direct Graphical Model are open source software. Netica is free for networks less than 15 nodes. A more complete list is available in Wikipedia under "Bayesian Networks." OpenBUGS► Stan► Direct Graphical Models► OpenMarkov► Graphical Models Toolkit► PyMC► Genie Smile► SamIam► Bayes Server► AIspace► BayesiaLab► Hugin► AgenaRisk► dVelox► System Modeler► UnBBayes► Uninet► Tetrad► Dezide► Netica► Question 1: Draw networks based on the following independence assumptions.
When directed networks are possible, give formulas for predicting
the last variable in the networks from marginal and pair-wise
conditional probabilities. Keep in mind that absence of
independence assumption implies dependence.
Review► Wang's
Teach One►
Question 2: Write an SQL code to calculate the probability of negative outcome in the situation
where the patient is severely ill and has not signed a "Do Not
Resuscitate" (DNR) order. Note that probabilities for
events that are mutually exclusive and exhaustive should add up to
one.
Data►
Bushra's Teach One►
Anto's Teach One►
Slides►
SQL► Question 3: Redo problem 2 in Netica or other software and verify the accuracy of your answer. To accomplish this, organize the 4-node network inside Netica and direct the links between the nodes, as in the graph structure in question 3. Then for every node, enter the table of probabilities as per tables given in Question 3. For example, for the DNR node enter the two probabilities of 0.1 and 0.9 into the Table within the node for DNR. Once the entire network (the graph and the related probabilities) has been entered into Netica, evaluate the risks for a patient who is severely ill and has not signed a "Do Not Resuscitate" order. Netica► Shruti's Teach One► Usman's Teach One► Question 4: If you were using logistic or ordinary regression equations, write what set of equations are represented by the following network:
In each instance write all the variables that are in the regression equation. These include the response (dependent) and the independent variable. Mark with * the independent variables that have a statistically significant relationship with the response variable. For example, LTH is regressed on all variables that precede it which are DME, CL, P and H. But only P and H have a statistically significant relationship with LTH. This regression can be shown as: LTH = a + b DME + c CL + d P* + e H* Insights into regressions and network models YouTube► Tutorial► Question 5: Construct a Bayesian probability network model that would
predict success with antidepressants. A network model will include
variables, and mediators of the effect of variables, on response to
the antidepressant. Include all baseline diagnoses and gender as
covariates. Assume that gender occurs before baseline
diagnoses. Baseline diagnoses occur before any treatment. Assume that antidepressant treatments occur
before report of remission and in the
following order: Remission should be considered an end node. Gender is a
root node. All other variables, e.g. diagnoses, could be either
root or intermediary nodes but all occur prior to use of
antidepressant. The antidepressants that were given prior to an
antidepressant should be used as a covariate. The data has been
modified to report per person data, without visit-based weekly data.
Data►
The following two images show two networks derived at different levels of Lambda, one at "lambda.min" and another at "lambda.1se". The network on the left shows that citalopram has an impact on remission. The network on the right shows that it does not, i.e. there is no direct line connecting citalopram to remission.
Question 6: Using the data provided and Netica software, construct a network model of COVID-19, Influenza, and other upper respiratory infections symptoms. Create a node at the center with three levels. Web Calculator► Background► Data► Data (no missing values)► Ghaida Alsadah's Teach One►
Question 7: The attached data show the percent of diabetes in different 2,228 counties within
United States in 2010, 2011, and 2012 years. We want to
understand if access to food stores affects diabetes. Create the network model,
using data from repeated LASSO regressions. The
first regression will be diabetes in 2012 on all 2011 variables.
Other LASSO regressions will have as response/dependent variable the
statistically significant variables in the previous regression
regressed on all 2010 variables. Draw the network model using Netica.
Stratify
the parents in Markov blanket of diabetes in 2012; calculate the impact of access
to quality food stores in 2011 on diabetes using stratified
covariate balancing.
Data►
Instruction for SCB►
New SCB Code►
Netica►
Answer►
Sean's Teach One► The following shows one possible model and not necessarily the model
you will construct with your data. This model was organized without
race and education levels higher than 1. Question 8: Inside an electronic health record, there are data on outcomes of a particular intervention. Using the network drawn below, write the equations that would allow you to estimate what would happen if the intervention was not given. First, write an equation for each node in the network based on variables that precede it. For example, the regression equation for predicting whether there is an adverse event is given by the equation: Outcome = a + b Treatment + c Severity Second, set the variables that change across these equations to the relevant values. For example, set Treatment to be zero. Velosky's Teach One►
Question 8: The following graph was used to simulate data on bundling payment for total hip fracture treatment: Recover the original network using LASSO regression and calculate the causal impact of H on BP using Netica. Data► Joanne Min's Teach One► Code►
MoreFor additional information (not part of the required reading), please see the following links:/p>
This page is part of the course on Comparative Effectiveness by Farrokh Alemi, Ph.D. Course Home► Email► |