arXiv 1 Mar 2024 · Econometrics
arXiv:2403.00422 · PDF · DOI · OpenAlex · Extracted main text
Interval identification of parameters such as average treatment effects, average partial effects and welfare is particularly common when using observational data and experimental data with imperfect compliance due to the endogeneity of individuals' treatment uptake. In this setting, the researcher is typically interested in a treatment or policy that is either selected from the estimated set of best-performers or arises from a data-dependent selection rule. In this paper, we develop new inference tools for interval-identified parameters chosen via these forms of selection. We develop three types of confidence intervals for data-dependent and interval-identified parameters, discuss how they apply to several examples of interest and prove their uniform asymptotic validity under weak assumptions.
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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 | Han, S (2024) Optimal dynamic treatment regimes and partial welfare ordering self | 1.000 | 13 | 3 | 100% |
| 2 | Balke, A. and Pearl, J (2011) Nonparametric bounds on causal effects from partial compliance data | 1.000 | 7 | 3 | 100% |
| 3 | Christensen, T., Moon, H. R., and Schorfheide, F (2023) Optimal decision rules when payoffs are partially identified | 1.000 | 6 | 3 | 100% |
| 4 | Balke, A. and Pearl, J (1997) Bounds on treatment effects from studies with imperfect compliance | 1.000 | 5 | 3 | 100% |
| 5 | McCloskey, A (2024) Hybrid confidence intervals for informative uniform asymptotic inference after model selection self | 0.894 | 7 | 4 | 71% |
| 6 | Andrews, I., Roth, J., and Pakes, A (2023) Inference for linear conditional moment inequalities | 0.874 | 8 | 2 | 100% |
| 7 | Han, S. and Yang, S (2024) A computational approach to identification of treatment effects for policy evaluation self | 0.874 | 6 | 2 | 100% |
| 8 | Kivaranovic, D. and Leeb, H (2021) On the length of post-model-selection confidence intervals conditional on polyhedral constraints | 0.843 | 3 | 3 | 100% |
| 9 | Andrews, I., Kitagawa, T., and McCloskey, A (2024) Inference on winners self | 0.817 | 11 | 3 | 55% |
| 10 | Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E (2016) Exact post-selection inference, with application to the LASSO | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 34 scored citations.