Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies
Causal Question / Estimand
What is the average (“typical”) causal effect of a treatment versus a control, defined as the average of the unit-level differences in Potential-Outcomes ? See Causal-Estimand.
Identification Strategy
The seminal statement of the potential-outcomes framework for causal inference. Rubin defines the causal effect through the two responses a unit would show under treatment and under control, then asks when data can estimate it. Randomization yields unbiased estimates directly; in nonrandomized studies, causal effects can still be estimated by controlling extraneous variation through matching and covariance (regression) adjustment, provided the right assumptions hold.
Key Assumptions
Potential-Outcomes, Randomization (when available), Ignorability (implicit, for the nonrandomized case), Overlap (comparable treated/control units for matching and adjustment).
Threats to Validity
Nonrandomized estimates are biased when assignment depends on unobserved determinants of the outcome (ignorability fails), or when matching/adjustment models are misspecified. Rubin’s stance: prefer randomization, but careful nonrandomized analysis is “a reasonable and necessary procedure in many cases.”
Setting / Data
n/a — methodological. Motivating examples drawn from educational and social-science program evaluation (e.g. compensatory reading programs).
Key Claims
- Introduces Potential-Outcomes as the basis for defining causal effects, not merely estimating associations.
- Randomization should be employed whenever possible — but the claim that only randomized experiments yield useful causal estimates is untenable.
- Matching and covariance adjustment are analysed as devices for estimating causal effects from nonrandomized data.
Connections
- See also: Holland1986-StatisticsAndCausalInference, Rubin1977-AssignmentOnCovariate, Rubin1980-RandomizationAnalysis, Foundations
Citation
Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688–701.