Resources for Causal Reasoning in Health Services Research

Category Archives: General

Lecture 1 in Causal attribution

Main message

Framework for drawing causal inference from observational studies:

  1. Treatment groups
  2. Patient-level outcome
  3. Summary measure of outcome
  4. Difference in outcome among groups
  5. Attribution to group membership
  6. Factors to control
  7. Factors we should not control



Bidirectional arrows in DAGs

Is there a way to graphically depict two intervening variables that influence each other in a DAG? By definition, it is clear that a DAG is acyclic and cannot include bi-directional arrows. Yet, I wonder if there is way to allow for the inclusion of this type of relationship in a causal model.

Thank-you! More

Causal perspective in CER

Preparing a presentation at SPPH, I used Pearl’s counterfactual probabilities in the context of coronary revascularization.


Counterfactuals and CER

In the patient care setting, we can only observe one possible treatment and one potential outcome for a given patient. Therefore, establishing the causal effect of treatment requires comparing the observed outcome and counterfactual outcome (i.e. a potential outcome for a counterfactual treatment).

Attribution in causal reasoning

I was presenting on the causal perspective in CER today, and a question came about connection between the claim that deaths are attributed to exposure and the claim that these deaths could be avoided had the exposure been eliminated. More

Assumptions in causal graphs

I was presenting today on causal diagrams, and a question came whether the assumption behind an arrow is of associational or causal nature. More

Message from the literature

Causal reasoning could be narrowed to deducing the causal effect from a set of conditional probabilities defining joint variation of random variables.