arXiv 25 May 2026 · Econometrics
arXiv:2605.25483 · PDF · DOI · OpenAlex · Extracted main text
The estimation of causal effects using quasiexperiments often relies on the use of unusual or serendipitous sources of exogenous variation. When the goal is estimating the same causal effects across many different settings, the same unusual exogenous variation often does not exist in all settings, and the only available form of identification is selection-on-observables, which relies on a conditional indepdendence assumption. Partial identification is especially valuable in this context, as it allows conditional independence to not hold perfectly. This paper proposes a method that sharpens the jointly identified set of causal effects across many settings by making use of unobserved relationships between omitted variable biases across settings.
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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 | Cinelli, C., Hazlett, C (2019) Making sense of sensitivity: Extending omitted variable bias | 0.693 | 10 | 1 | 100% |
| 2 | Chernozhukov, V., Cinelli, C., Newey, W.K., Sharma, A., Syrgkanis, V (2026) Long story short: Omitted variable bias in causal machine learning | 0.644 | 4 | 1 | 100% |
| 3 | Imbens, G.W., Manski, C.F (2004) Confidence intervals for partially identified parameters | 0.511 | 2 | 1 | 100% |
| 4 | Oster, E (2019) Unobservable selection and coefficient stability: Theory and evidence | 0.511 | 2 | 1 | 100% |
| 5 | Rambachan, A., Roth, J (2023) A more credible approach to parallel trends | 0.511 | 2 | 1 | 100% |
| 6 | Stoye, J (2009) More on confidence intervals for partially identified parameters | 0.511 | 2 | 1 | 100% |
| 7 | Wager, S., Athey, S (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.511 | 2 | 1 | 100% |
| 8 | Yadlowsky, S., Namkoong, H., Basu, S., Duchi, J., Tian, L (2022) Bounds on the conditional and average treatment effect with unobserved confounding factors | 0.511 | 2 | 1 | 100% |
| 9 | Altonji, J.G., Elder, T.E., Taber, C.R (2005) Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools | 0.405 | 1 | 1 | 100% |
| 10 | Chen, K., Zhang, J., Wang, B., Small, D.S (2023) A differential effect approach to partial identification of treatment effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 22 scored citations.