arXiv 6 May 2025 · Statistics — Methodology
arXiv:2505.03937 · PDF · DOI · OpenAlex · Extracted main text
Experimental designs are fundamental for estimating causal effects. In some fields, within-subjects designs, which expose participants to both control and treatment at different time periods, are used to address practical and logistical concerns. Counterbalancing, a common technique in within-subjects designs, aims to remove carryover effects by randomizing treatment sequences. Despite its appeal, counterbalancing relies on the assumption that carryover effects are symmetric and cancel out, which is often unverifiable a priori. In this paper, we formalize the challenges of counterbalanced within-subjects designs using the potential outcomes framework. We introduce sequential exchangeability as an additional identification assumption necessary for valid causal inference in these designs. To address identification concerns, we propose diagnostic checks, the use of washout periods, and covariate adjustments, and alternative experimental designs to counterbalanced within-subjects design. Our findings demonstrate the limitations of counterbalancing and provide guidance on when and how within-subjects designs can be appropriately used for causal inference.
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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 | Clifford, S., G. Sheagley, and S. Piston (2021) Increasing precision without altering treatment effects: Repeated measures designs in survey experiments | 0.874 | 6 | 2 | 100% |
| 2 | Maxwell, S. E., H. D. Delaney, and K. Kelley (2017) Designing experiments and analyzing data: A model comparison perspective | 0.811 | 4 | 2 | 100% |
| 3 | Kenward, M. G. and B. Jones (2007) 15 design and analysis of cross-over trials | 0.644 | 2 | 2 | 100% |
| 4 | Holland, P. W (1986) Statistics and causal inference | 0.511 | 2 | 1 | 100% |
| 5 | Lane, J. N., L. Boussioux, C. Ayoubi, Y. H. Chen, C. Lin, R. Spens,… (2024) The narrative ai advantage? a field experiment on generative ai-augmented evaluations of early-stage innovations | 0.511 | 2 | 1 | 100% |
| 6 | Tamura, R. N., X. Huang, and D. D. Boos (2011) Estimation of treatment effect for the sequential parallel design | 0.405 | 1 | 1 | 100% |
| 7 | Whitehead, J., Y. Desai, and T. Jaki (2020) Estimation of treatment effects following a sequential trial of multiple treatments | 0.405 | 1 | 1 | 100% |
| 8 | Zhou, Q., P. A. Ernst, K. L. Morgan, D. B. Rubin, and A. Zhang (2018) Sequential rerandomization | 0.405 | 1 | 1 | 100% |
| 9 | Charness, G., U. Gneezy, and M. A. Kuhn (2012) Experimental methods: Between-subject and within-subject design | 0.405 | 1 | 1 | 100% |
| 10 | Imai, K., L. Keele, and D. Tingley (2010) A general approach to causal mediation analysis | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.