arXiv 23 Jan 2026 · Statistics — Methodology
arXiv:2601.16749 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a finite population framework for analyzing causal effects in settings with imperfect compliance where multiple treatments affect the outcome of interest. Two prominent examples are factorial designs and panel experiments with imperfect compliance. I define finite population causal effects that capture the relative effectiveness of alternative treatment sequences. I provide nonparametric estimators for a rich class of factorial and dynamic causal effects and derive their finite population distributions as the sample size increases. Monte Carlo simulations illustrate the desirable properties of the estimators. Finally, I use the estimator for causal effects in factorial designs to revisit a famous voter mobilization experiment that analyzes the effects of voting encouragement through phone calls on turnout.
appendix boundary found by appendix_titled_section at “Appendix A - Main Proofs” · 49% 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 | Matthew Blackwell (2017) Instrumental Variable Methods for Conditional Effects and Causal Interaction in Voter Mobilization Experiments | 1.000 | 6 | 3 | 100% |
| 2 | Howard S. Bloom (1984) Accounting for No-Shows in Experimental Evaluation Designs | 0.811 | 4 | 2 | 100% |
| 3 | Nickerson, David W (2007) Quality Is Job One: Professional and Volunteer Voter Mobilization Calls | 0.811 | 4 | 2 | 100% |
| 4 | Bojinov, Iavor and Rambachan, Ashesh and Shephard, Neil (2021) Panel experiments and dynamic causal effects: A finite population perspective | 0.754 | 7 | 4 | 43% |
| 5 | Matthew Blackwell and Nicole E. Pashley (2023) Noncompliance and Instrumental Variables for 2K Factorial Experiments | 0.737 | 3 | 2 | 100% |
| 6 | Iavor Bojinov and Neil Shephard (2019) Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading | 0.644 | 2 | 2 | 100% |
| 7 | Kang, Hyunseung and Peck, Laura and Keele, Luke (2018) Inference for Instrumental Variables: A Randomization Inference Approach | 0.644 | 2 | 2 | 100% |
| 8 | Gerber, Alan S. and Green, Donald P (2000) The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment | 0.405 | 1 | 1 | 100% |
| 9 | Imbens, Guido W. and Rubin, Donald B (2015) REGULAR ASSIGNMENT MECHANISMS WITH NONCOMPLIANCE: ANALYSIS | 0.405 | 1 | 1 | 100% |
| 10 | James Robins (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy w… | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 19 scored citations.