Nicolas Apfel, Helmut Farbmacher, Rebecca Groh, Martin Huber, Henrika Langen
arXiv 10 Jul 2022 · Econometrics
arXiv:2207.04481 · PDF · DOI · OpenAlex · Extracted main text
Under an endogenous binary treatment with heterogeneous effects and multiple instruments, we propose a two-step procedure for identifying complier groups with identical local average treatment effects (LATE) despite relying on distinct instruments, even if several instruments violate the identifying assumptions. We use the fact that the LATE is homogeneous for instruments which (i) satisfy the LATE assumptions (instrument validity and treatment monotonicity in the instrument) and (ii) generate identical complier groups in terms of treatment propensities given the respective instruments. We propose a two-step procedure, where we first cluster the propensity scores in the first step and find groups of IVs with the same reduced form parameters in the second step. Under the plurality assumption that within each set of instruments with identical treatment propensities, instruments truly satisfying the LATE assumptions are the largest group, our procedure permits identifying these true instruments in a data driven way. We show that our procedure is consistent and provides consistent and asymptotically normal estimators of underlying LATEs. We also provide a simulation study investigating the finite sample properties of our approach and an empirical application investigating the effect of incarceration on recidivism in the US with judge assignments serving as instruments.
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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 | Ke, Z. T., J. Fan, and Y. Wu (2015) Homogeneity pursuit | 0.843 | 5 | 3 | 60% |
| 2 | Apfel, N. and X. Liang (2021) Agglomerative Hierarchical Clustering for Selecting Valid Instrumental Variables self | 0.822 | 9 | 3 | 56% |
| 3 | Loeffler, C. E. and D. S. Nagin (2021) The impact of incarceration on recidivism | 0.811 | 4 | 2 | 100% |
| 4 | Ward, J. H. J (1963) Hierarchical Grouping to Optimize an Objective Function | 0.811 | 4 | 2 | 100% |
| 5 | Guo, Z., H. Kang, T. T. Cai, and D. S. Small (2018) Confidence Intervals for Causal Effects with Invalid Instruments by Using Two-Stage Hard Thresholding with Voting | 0.737 | 3 | 3 | 67% |
| 6 | Angrist, J., G. Imbens, and D. Rubin (1996) Identification of causal effects using instrumental variables | 0.737 | 3 | 2 | 100% |
| 7 | Bhuller, M., G. B. Dahl, K. V. Lken, and M. Mogstad (2020) Incarceration, recidivism, and employment | 0.737 | 3 | 2 | 100% |
| 8 | Imbens, G. W. and J. Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Sun, Z. and K. Wüthrich (2022) Pairwise Valid Instruments | 0.737 | 3 | 2 | 100% |
| 10 | Kang, H., A. Zhang, T. T. Cai, and D. S. Small (2016) Instrumental Variables Estimation with Some Invalid Instruments and Its Application to Mendelian Randomization | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2407.04448 | 0.405 | 1 | 1 |