Nonparametric Estimation of Average Treatment Effects under Exogeneity
Causal Question / Estimand
The ATE and ATT for a binary treatment under selection on observables — and a unified review of the estimators that target them.
Identification Strategy
Identification of treatment effects follows from two assumptions: unconfoundedness (Ignorability) — assignment is independent of potential outcomes given covariates — and Overlap — every covariate value has positive probability of both treatment states. Together these point-identify the ATE/ATT nonparametrically. The review then organizes the estimators that exploit this: regression on covariates, Propensity-Score methods (weighting, blocking/subclassification), matching, and doubly robust combinations, and discusses their large-sample efficiency (the efficiency bound) and variance estimation.
Key Assumptions
Ignorability (unconfoundedness / exogeneity), Overlap (positivity), SUTVA, and — for the propensity-score route — correct specification or nonparametric estimation of $e(X)$.
Threats to Validity
Failure of unconfoundedness (unobserved confounders); thin or violated overlap, where estimates are driven by extrapolation and variance explodes; misspecified propensity or regression models. Notes the role of trimming and overlap diagnostics.
Setting / Data
n/a — methodological review (econometric survey).
Key Claims
- Under unconfoundedness + overlap, ATE/ATT are nonparametrically identified, and a large family of estimators (regression, matching, weighting, blocking) target the same parameter.
- The propensity score reduces dimensionality but does not relax the identifying assumptions; combining outcome and propensity models yields efficiency/robustness.
- Overlap is as consequential as unconfoundedness — limited overlap is a central practical obstacle.
Connections
- Builds on: Potential-Outcomes, Ignorability, Overlap, Rubin1977-AssignmentOnCovariate
- Companion practice guide: Imbens2015-MatchingMethodsInPractice
- Underlies the propensity-score DiD of Abadie2005-SemiparametricDiD; surveyed alongside ImbensWooldridge2009-ProgramEvaluation. See also PSM
Citation
Imbens, G. W. (2004). Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review. The Review of Economics and Statistics, 86(1), 4–29.