Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness
In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the...
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| Format: | Article |
| Language: | English |
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De Gruyter
2020-12-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2020-0011 |
| _version_ | 1818597383804551168 |
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| author | Huntington-Klein Nick |
| author_facet | Huntington-Klein Nick |
| author_sort | Huntington-Klein Nick |
| collection | DOAJ |
| description | In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples. |
| first_indexed | 2024-12-16T11:46:56Z |
| format | Article |
| id | doaj.art-65e100bca46f488486ae8a73fdc45876 |
| institution | Directory Open Access Journal |
| issn | 2193-3677 2193-3685 |
| language | English |
| last_indexed | 2024-12-16T11:46:56Z |
| publishDate | 2020-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-65e100bca46f488486ae8a73fdc458762022-12-21T22:32:49ZengDe GruyterJournal of Causal Inference2193-36772193-36852020-12-018118220810.1515/jci-2020-0011jci-2020-0011Instruments with Heterogeneous Effects: Bias, Monotonicity, and LocalnessHuntington-Klein Nick0Seattle University, SeattleUnited States of AmericaIn Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity, which is generally not the average treatment effect among those affected by the instrument. I then describe a simple set of data-driven approaches to modeling variation in the effect of the instrument. These approaches identify a Super-Local Average Treatment Effect (SLATE) that weights treatment effects by the corresponding instrument effect more heavily than LATE. Even when first-stage heterogeneity is poorly modeled, these approaches considerably reduce the impact of small-sample bias compared to standard IV and unbiased weak-instrument IV methods, and can also make results more robust to violations of monotonicity. In application to a published study with a strong instrument, the preferred approach reduces error by about 19% in small (N ≈ 1, 000) subsamples, and by about 13% in larger (N ≈ 33, 000) subsamples.https://doi.org/10.1515/jci-2020-0011causal inferenceobservational studiescomputational methodseconomics62d2091-08 |
| spellingShingle | Huntington-Klein Nick Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness Journal of Causal Inference causal inference observational studies computational methods economics 62d20 91-08 |
| title | Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness |
| title_full | Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness |
| title_fullStr | Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness |
| title_full_unstemmed | Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness |
| title_short | Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness |
| title_sort | instruments with heterogeneous effects bias monotonicity and localness |
| topic | causal inference observational studies computational methods economics 62d20 91-08 |
| url | https://doi.org/10.1515/jci-2020-0011 |
| work_keys_str_mv | AT huntingtonkleinnick instrumentswithheterogeneouseffectsbiasmonotonicityandlocalness |