arXiv 10 Oct 2020 · Econometrics · publishedMarketing Science (2023) · 5 citations (OpenAlex)
arXiv:2010.05117 · PDF · DOI · OpenAlex · Extracted main text
Randomized controlled trials generate experimental variation that can credibly identify causal effects, but often suffer from limited scale, while observational datasets are large, but often violate desired identification assumptions. To improve estimation efficiency, I propose a method that leverages imperfect instruments - pretreatment covariates that satisfy the relevance condition but may violate the exclusion restriction. I show that these imperfect instruments can be used to derive moment restrictions that, in combination with the experimental data, improve estimation efficiency. I outline estimators for implementing this strategy, and show that my methods can reduce variance by up to 50%; therefore, only half of the experimental sample is required to attain the same statistical precision. I apply my method to a search listing dataset from Expedia that studies the causal effect of search rankings on clicks, and show that the method can substantially improve the precision.
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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 | Ursu, R. M (2018) The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions | 1.000 | 7 | 3 | 100% |
| 2 | Lewis, R. A. and Rao, J. M (2015) The Unfavorable Economics of Measuring the Returns to Advertising* | 0.811 | 4 | 2 | 100% |
| 3 | Imbens, G. W. and Lancaster, T (1994) Combining Micro and Macro Data in Microeconometric Models | 0.737 | 3 | 2 | 100% |
| 4 | Li, L., Chen, S., Kleban, J., and Gupta, A (2015) Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study | 0.644 | 2 | 2 | 100% |
| 5 | Nevo, A. and Rosen, A. M (2010) Identification With Imperfect Instruments | 0.644 | 2 | 2 | 100% |
| 6 | Wang, X., Bendersky, M., Metzler, D., and Najork, M (2016) Learning to Rank with Selection Bias in Personal Search | 0.644 | 2 | 2 | 100% |
| 7 | Deng, A., Xu, Y., Kohavi, R., and Walker, T (2013) Improving the sensitivity of online controlled experiments by utilizing pre-experiment data | 0.585 | 3 | 1 | 100% |
| 8 | Peysakhovich, A. and Lada, A (2016) Combining observational and experimental data to find heterogeneous treatment effects | 0.585 | 3 | 1 | 100% |
| 9 | Bertrand, M., Karlan, D., Mullainathan, S., Shafir, E., and Zinman, J (2010) What's Advertising Content Worth? Evidence from a Consumer Credit Marketing Field Experiment* | 0.511 | 4 | 2 | 25% |
| 10 | Chen, M. K., Chevalier, J. A., Rossi, P. E., and Oehlsen, E (2019) The Value of Flexible Work: Evidence from Uber Drivers | 0.511 | 3 | 2 | 33% |
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