Learn Before
Concept
Evaluating Counterfactuals in Structural Causal Models
Evaluating a counterfactual using a Structural Causal Model (SCM) involves three sequential steps, which are valid regardless of whether the functional relationships are linear or non-linear:
- Abduction: Use evidence (observations) to estimate the values of the unobserved background (exogenous) variables .
- Action: Use the do-operator to intervene on the model, setting the value of the antecedent variable (e.g., setting ), which modifies the structural equations by removing the equations that originally determined that variable.
- Prediction: Use the modified structural equations and the estimated values of to calculate the counterfactual values of the outcome variables (e.g., ).
0
1
Updated 2026-06-21
Contributors are:
Who are from:
Tags
Data Science
Related
Evaluating Counterfactuals in Structural Causal Models
Examples of Counterfactual Questions
Hume's Redefinition of Causation
Counterfactuals and the law
Rubin Casual Model
Effect of Treatment on the Treated (ETT)
Counterfactual youtube “bootcamp”
Bickel's Definition of Discrimination
The Law of Counterfactuals
Evaluating Counterfactuals in Structural Causal Models