Lecture 10 Flashcards
(32 cards)
What is Causality?
Causal modelling in observational data.
Where is Causality crucial?
In Responsible and Explainable AI.
What is the Classical rigid approach?
Causal interference is only possible in RCT’s (Randomized Clinical Trials).
How does a RCT work?
All patients are randomly assigned to a control group and an experiment group.
What is the goal of the Classical rigid approach?
To estimate the effect of the IV (Independent Variable) on the DV (Dependent Variable).
What is the Independent Variable?
Usually an intervention, exposure or treatment?
What is the Dependent Variable?
Usually represents a survival time, test score, measurement or a binary outcome.
What can we assume due to randomisation (Classical rigid approach)?
That both groups are identical with respect to all measured/unmeasured variables (except the IV).
What causes the differences between C and E with respect to the DV?
The IV (cause-effect relation).
Why is RCT often not possible? (4 reasons)
Unethical, logically impossible, too expensive, too timeconsuming.
What kinds of studies are done in a RCT without randomisation?
Observational studies.
What can we not assume in a RCT without randomisation?
That both groups are identical with respect to all (un)measured variables.
What does the absence of identity affect and jeopardize (RCT without randomisation)?
The established cause-effect relation is affected and the validity of the research is jeopardized (bias could occur).
What is the Confounding Variable?
A variable that influences both the dependent and the independent variable.
What is a disadvantage of the Confounding Variable?
It may cause a false association between IV and DV.
What may the controlling for the confounder do? (Confounding Variable)
It may drop the association between IV and DV to zero.
What may the 3rd variable do? (2 things) (Confounding variable)
- It may weaken the effect of IV on DV.
2. It may cause the sign to switch (positive negative).
Are Correlations / Associations enough to identify a confounding variable?
No.
What is a Mediation Variable?
A mediation model is used to identify and explain the mechanism/process that underlies an observed relationship between an IV and DV by including a 3rd variable, the mediation variable.
What kind of relation can you find between IV and DV with the Mediation model?
An Indirect causal relation.
What is the Mediation Variable part of? What does it serve for?
It is part of the causal path and serves to clarify the nature of the relationship between IV and DV.
When do Mediation analysis work best?
When the IV and DV don’t have an obvious connection.
What is Full Mediation?
When the inclusion of the mediation variable drops the relationship between IV and DV to zero.
What is Partial Mediation?
When the inclusion of the mediation variable accounts for some of the relationship between IV and DV (direct and indirect effects).
