Contamination Bias in Linear Regressions
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| _version_ | 1866913628100231168 |
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| author | Goldsmith-Pinkham, Paul Hull, Peter Kolesár, Michal |
| author_facet | Goldsmith-Pinkham, Paul Hull, Peter Kolesár, Michal |
| contents | We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects -- instead, estimates of each treatment's effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_05024 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Contamination Bias in Linear Regressions Goldsmith-Pinkham, Paul Hull, Peter Kolesár, Michal Econometrics Methodology We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects -- instead, estimates of each treatment's effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores. |
| title | Contamination Bias in Linear Regressions |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2106.05024 |