4.12 ITE: Estimation

• Individual-level Treatment Effect: Difference in potential outcome for unit i at time t post-treatment
• ITE is unobservable because units can not receive both treatment and control (i.e., all values of treatment variable)
  • Aspirin → headache: Simone has headache and takes aspirin

Unit	Di (Aspirin: Yes/No)	Yi (Pain: Yes/No)	Yi1 (Pain | Aspirin)	Yi0 (Pain | NoAspirin)
Simone	1	0	0	?

• ITE forces us to think about this process in terms of counterfactuals and individual cases (Lam 2013)
• Table above divides the Y column into columns for treatment and control group
  • Q: What would the table look like if the treatment had more levels?
• Estimation requires filling in/replacing the missing counterfactual

References

Lam, Patrick Kenneth. 2013. “Estimating Individual Causal Effects.” PhD thesis, Harvard University.