Federico A. Bugni, Mengsi Gao, Filip Obradovic, Amilcar Velez
arXiv 12 Jun 2024 · Econometrics
arXiv:2406.08419 · PDF · DOI · OpenAlex · Extracted main text
Randomized controlled trials (RCTs) frequently utilize covariate-adaptive randomization (CAR) (e.g., stratified block randomization) and commonly suffer from imperfect compliance. This paper studies the identification and inference for the average treatment effect (ATE) and the average treatment effect on the treated (ATT) in such RCTs with a binary treatment. We first develop characterizations of the identified sets for both estimands. Since data are generally not i.i.d. under CAR, these characterizations do not follow from existing results. We then provide consistent estimators of the identified sets and asymptotically valid confidence intervals for the parameters. Our asymptotic analysis leads to concrete practical recommendations regarding how to estimate the treatment assignment probabilities that enter the estimated bounds. For the ATE bounds, using sample analog assignment frequencies is more efficient than relying on the true assignment probabilities. For the ATT bounds, the most efficient approach is to use the true assignment probability for the probabilities in the numerator and the sample analog for those in the denominator.
appendix boundary found by appendix_titled_section at “Appendix on identification” · 30% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bugni, F. A. and M. Gao (2023) Inference under Covariate-Adaptive Randomization with Imperfect Compliance self | 1.000 | 8 | 3 | 100% |
| 2 | Dupas, P., D. Karlan, J. Robinson, and D. Ubfal (2018) Banking the Unbanked? Evidence from Three Countries | 1.000 | 7 | 3 | 100% |
| 3 | Hirano, K., G. Imbens, and G. Ridder (2003) Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score | 0.843 | 3 | 3 | 100% |
| 4 | Huber, M., L. Laffers, and G. Mellace (2017) Sharp IV bounds on average treatment effects on the treated and other populations under endogeneity and noncompliance | 0.843 | 3 | 3 | 100% |
| 5 | Bugni, F. A., I. A. Canay, and A. M. Shaikh (2018) Inference under Covariate Adaptive Randomization self | 0.811 | 4 | 2 | 100% |
| 6 | Stoye, J (2009) More on Confidence Intervals for Partially Identified Parameters | 0.781 | 7 | 2 | 71% |
| 7 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.737 | 3 | 2 | 100% |
| 8 | Andrews, D. W. K. and G. Soares (2010) Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection | 0.644 | 2 | 2 | 100% |
| 9 | Bugni, F. A., M. Gao, F. Obradovic, and A. Velez (2025) On the power properties of inference for parameters with interval identified sets, ArXiv preprint arXiv:2407.20386 self | 0.644 | 2 | 2 | 100% |
| 10 | Hahn, J (1998) On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 32 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances | 0.405 | 1 | 1 |