arXiv 27 Feb 2023 · Econometrics · publishedManagement Science (2025) · 1 citations (OpenAlex)
arXiv:2302.13857 · PDF · DOI · OpenAlex · Extracted main text
Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance extant empirical approaches may not produce the estimands a decision maker needs to solve the problem of interest. For example, these experimental designs are common in digital advertising settings but typical methods do not yield effects that inform the intensive margin: how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches.
appendix boundary found by appendix_command · 63% of the source is main text. Read the extracted text to check this.
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 | Brinch, C. N., Mogstad, M., and Wiswall, M (2017) Beyond LATE with a discrete instrument | 0.899 | 11 | 4 | 73% |
| 2 | Gordon, B. R., Zettelmeyer, F., Bhargava, N., and Chapsky, D (2019) A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook self | 0.843 | 4 | 4 | 75% |
| 3 | Mogstad, M., Santos, A., and Torgovitsky, A (2018) Using instrumental variables for inference about policy relevant treatment parameters | 0.843 | 4 | 3 | 75% |
| 4 | Heckman, J. J. and Vytlacil, E (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.737 | 3 | 2 | 100% |
| 5 | Gordon, B. R., Moakler, R., and Zettelmeyer, F (2023) Close enough? A large-scale exploration of non-experimental approaches to advertising measurement self | 0.644 | 2 | 2 | 100% |
| 6 | Johnson, G. A., Lewis, R. A., and Reiley, D. H (2016) Location, location, location: Repetition and proximity increase advertising effectiveness | 0.644 | 2 | 2 | 100% |
| 7 | Sahni, N. S., Narayanan, S., and Kalyanam, K (2019) An experimental investigation of the effects of retargeted advertising: The role of frequency and timing | 0.644 | 2 | 2 | 100% |
| 8 | Heckman, J. J., Urzua, S., and Vytlacil, E (2006) Understanding instrumental variables in models with essential heterogeneity | 0.511 | 2 | 2 | 50% |
| 9 | Manski, C. F (1997) Monotone treatment response | 0.511 | 2 | 2 | 50% |
| 10 | Zigler, C. M., Watts, K., Yeh, R. W., Wang, Y., Coull, B. A., and Do… (2013) Model feedback in Bayesian propensity score estimation | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 58 scored citations.