 Introduction to causal inference Read
1►
Read 2►
Video►
Slides►
 Causal impact, dseparation and backdoors
Slides►
 Blocking backdoor
Read►
Slides►
 Example of back door criterion
Read►
 Minimizing stratification through backdoor criterion
Read►
Slides►
 Network analysis using Grow Shrink & Hiton & Sequence
R code► Slides►
Soylu's Video►
 Network analysis using Poisson regression
Read►
Dispersion►
 Optimizing stratification Read►
 Impact of sequence on accuracy of network learning algorithms
Read►
Assignment
For this assignment you can use any statistical package. Work can be done in group's of two students but you cannot work with a student that you have previously teamed up with.
Question 1: 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.
Question 2: The following graph was used to simulate data on bundling
payment for total hip fracture
treatment:

Recover the original network and calculate the causal
impact of H on BP.
Data►
R Code►
Detail R Code►

If you were using logistic or ordinary regression
equations, write what set of equations are represented by the above
network. In each instance write all the variables that are in
the regression equation and the 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*
In the above equation, the
statistically significant relationships are shown with a star (*).
A missing star indicates an insignificant relation. Using the
data, estimates the parameters of each of the regressions. Can
these set of equations be used to create the network. In how
many ways does the regression equations differ from the network model
in the graph.
Question 3: Construct a decision aid
for selection among antidepressants.
 Read about the STAR*D study protocol.
Review►
 Download data. Use instructor's last name as password.
Data►

Repeat the following analysis for at least 5 antidepressant(s).
Separate analysis must be done for each antidepressant or
antidepressant combination (shown in variable CONCAT).
 Create data sets for each antidepressant(s) combination. This data sets will include a patient several times,
if the patient received different combinations of antidepressants
over time. Group By Concat and ID variable to
remove the weekly data. If the patient received the
antidepressant(s) combination, assign it a value of 1 and
otherwise, when they received other combination of
antidepressant(s) assign it a value of 0.
 Identify the parents in the Markov Blanket of each
antidepressant. You can use logistic
regression to do this. For each antidepressant use all
variables that precede it as independent variables in the
regression. Use the variables that are significant
predictors of the antidepressant as the parents in the Markov
Blanket of the antidepressant.
 Stratify treatment and exclude from the list of parents in the
Markov Blanket any variable not related to remission (measured as
referred to followup). Calculate the unconfounded impact of antidepressant(s) on
remission. Stratify the remaining variables in the parents in
the Markov Blanket of treatment and calculate the impact of
antidepressant(s) on remission.
 Evaluate for a patient with PTSD and neurological disorders
which of the 5 antidepressant(s) combination is most likely to lead to remission.
More
For additional information (not part of the required reading), please see the following links:

Causal inference based on counterfactuals Read►

Causation, bias and confounding Read►

Causal analysis in epidemiology
Read►
 Counterfactual thinking deficit
PubMed►
 Counterfactual thinking in moral judgment
PubMed►
 Counterfactual reasoning and pretend play PubMed►
 The human disease network Read►

Causal reasoning in humans Read►

Farhan's lecture on backdoors Video►
This page is part of the course on Comparative Effectiveness by Farrokh Alemi, PhD Home►
Email►