arXiv 29 Dec 2025 · Econometrics
arXiv:2512.23854 · PDF · DOI · OpenAlex · Extracted main text
To evaluate the effectiveness of a counterfactual policy, it is often necessary to extrapolate treatment effects on compliers to broader populations. This extrapolation relies on exogenous variation in instruments, which is often weak in practice. This limited variation leads to invalid confidence intervals that are typically too short and cannot be accurately detected by classical methods. For instance, the F-test may falsely conclude that the instruments are strong. Consequently, I develop inference results that are valid even with limited variation in the instruments. These results lead to asymptotically valid confidence sets for various linear functionals of marginal treatment effects, including LATE, ATE, ATT, and policy-relevant treatment effects, regardless of identification strength. This is the first paper to provide weak instrument robust inference results for this class of parameters. Finally, I illustrate my results using data from Agan, Doleac, and Harvey (2023) to analyze counterfactual policies of changing prosecutors' leniency and their effects on reducing recidivism.
appendix boundary found by appendix_command · 37% 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 | Agan, A., J. L. Doleac, and A. Harvey (2023) Misdemeanor prosecution | 1.000 | 7 | 4 | 100% |
| 2 | Brinch, C. N., M. Mogstad, and M. Wiswall (2017) Beyond LATE with a discrete instrument | 0.956 | 8 | 4 | 88% |
| 3 | Andrews, I (2018) Valid two-step identification-robust confidence sets for GMM | 0.874 | 15 | 5 | 67% |
| 4 | Kleibergen, F (2005) Testing parameters in GMM without assuming that they are identified | 0.855 | 8 | 5 | 62% |
| 5 | Kline, P. and C. R. Walters (2019) On Heckits, LATE, and numerical equivalence | 0.843 | 4 | 3 | 75% |
| 6 | Chaudhuri, S. and E. Zivot (2011) A new method of projection-based inference in GMM with weakly identified nuisance parameters | 0.843 | 5 | 3 | 60% |
| 7 | Carneiro, P., J. J. Heckman, and E. J. Vytlacil (2011) Estimating marginal returns to education | 0.843 | 3 | 3 | 100% |
| 8 | Carneiro, P., J. J. Heckman, and E. J. Vytlacil (2010) Evaluating marginal policy changes and the average effect of treatment for individuals at the margin | 0.811 | 4 | 2 | 100% |
| 9 | Heckman, J. J. and E. Vytlacil (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.811 | 4 | 2 | 100% |
| 10 | Andrews, D. W (2017) Identification-robust subvector inference | 0.794 | 10 | 4 | 50% |
Showing the top 10 of 70 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 Sharp Test for the Judge Leniency Design | 0.405 | 1 | 1 |