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►


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.

A. 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.  In particular, write the equation to calculate the probability of adverse event conditioned on intervention not given.

B. 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►

C. 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. Protocol►
  2. Download data.  Use instructor's last name as password.  Must enter password twice. Data 2010► Data 2003►
  3. Assume that generalized anxiety, social phobia, alcohol abuse, and drug abuse lead to treatment with citalopram and treatment leads to remission.  Assume that remission is an end-node.
  4. Using independence tests identify nodes that are connected to each other.
  5. Identify paths that start from remission and end with treatment.
  6. Identify common effects in any triplet of nodes within the paths and remove the effect from consideration as a block.
  7. Identify a minimum set of nodes that block all paths.
  8. Use the blocks to stratify impact of treatment on remission.
  9. Describe whether citalopram causes remission.


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►