(Source) In causal inference, a counterfactual refers to what would have happened under different conditions or scenarios that did not occur (especially when comparing what actually happened i.e. the observed outcome). It involves considering “what if” scenarios to assess the impact of causal factors.
For a patient who is in the treatment group, what would happen if they had joined the control group? Thinking through counterfactual is a good part of causal inference, because
Example
Suppose you want to determine whether a new drug (Drug A) is effective in treating a particular medical condition. You conduct a clinical trial where half of the patients receive Drug A, and the other half receives a placebo (a fake treatment). After the trial, you observe that patients who received Drug A had a significantly higher recovery rate compared to those who received the placebo.
In causal inference, you may want to ask: “What would have happened to the group of patients who received the placebo if they had received Drug A instead?” This hypothetical scenario represents a counterfactual. It allows you to compare the observed outcome (patients who received Drug A had a higher recovery rate) with the counterfactual scenario (patients who received the placebo but hypothetically received Drug A) to assess the causal effect of Drug A on the outcome.
We would need statistical modeling to fill in predictions for counterfactual, to measure the impact between option A and B (e.g. the difference in outcomes meditation vs fasting), for causal inference later.
