Lecture: Counterfactual Framework  

 

Assigned Reading

  1. Introduction to causal inference Read 1► Read 2► Video► Slides►
  2. Causal impact, d-separation and backdoors Slides►
  3. Blocking backdoor Read► Slides►
  4. Example of back door criterion Read►
  5. Minimizing stratification through backdoor criterion Read► Slides►
  6. Network analysis using Grow Shrink & Hiton & Sequence R code► Slides► Soylu's Video►
  7. Network analysis using Poisson regression Read► Dispersion►
  8. Optimizing stratification Read►
  9. 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:
Bundled payments for total hip fracture

  • 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 signficant 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 signficant 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 signficant 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 model to predict what would have happened if patients who received citalopram in level 1 of the STAR*D experiment had received a different medication.   

  1. Read about the STAR*D study protocol. Review►
  2. Download data.  Use instructor's last name as password.  Data►
  3. Create data sets for each antidepressant combination (variable Concat).  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.
  4. Identify the parents in the Markov Blanket of each antidepressant.  You can use ordinary regression or logistic regression to do this. 
  5. Stratify treatment and exclude from the list of parents in the Markov Blanket any variable not related to remission (measured as referred to followup).
  6. 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.
  7. Repeat the analysis for at least 3 antidepressant(s).
  8. Evaluate for a patient with PTSD and neurological disorders which antidepressant is most likely to lead to best remission. 

More

For additional information (not part of the required reading), please see the following links:

  1. Causal inference based on counterfactuals Read►
  2. Causation, bias and confounding Read►
  3. Causal analysis in epidemiology Read►
  4. Counterfactual thinking deficit PubMed►
  5. Counterfactual thinking in moral judgment  PubMed►
  6. Counterfactual reasoning and pretend play PubMed►
  7. The human disease network Read►
  8. Causal reasoning in humans Read►
  9. Farhan's lecture on backdoors Video►

This page is part of the course on Comparative Effectiveness by Farrokh Alemi, PhD Home► Email►