Matthew A. Masten, Alexandre Poirier
arXiv 29 Jul 2017 · Statistics — Methodology · publishedEconometrica (2018) · 52 citations (OpenAlex)
arXiv:1707.09563 · PDF · DOI · OpenAlex · Extracted main text
Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.
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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 | Imbens, G. W (2003) Sensitivity to exogeneity assumptions in program evaluation | 1.000 | 6 | 3 | 100% |
| 2 | Rosenbaum, P. R. and D. B. Rubin (1983) Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome | 0.737 | 3 | 2 | 100% |
| 3 | Altonji, J. G., T. E. Elder, and C. R. Taber (2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools | 0.644 | 2 | 2 | 100% |
| 4 | Altonji, J. G., T. E. Elder, and C. R. Taber (2008) Using selection on observed variables to assess bias from unobservables when evaluating Swan-Ganz catheterization | 0.644 | 2 | 2 | 100% |
| 5 | Manski, C. F (2007) Identification for Prediction and Decision | 0.644 | 2 | 2 | 100% |
| 6 | Robins, J. M., A. Rotnitzky, and D. O. Scharfstein (2000) Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models, in | 0.644 | 2 | 2 | 100% |
| 7 | Imbens, G. W. and D. B. Rubin (2015) Causal Inference in Statistics, Social, and Biomedical Sciences | 0.585 | 3 | 1 | 100% |
| 8 | Cornfield, J., W. Haenszel, E. C. Hammond, A. M. Lilienfeld, M. B. S… (1959) Smoking and lung cancer: Recent evidence and a discussion of some questions | 0.511 | 2 | 1 | 100% |
| 9 | Masten, M. A. and A. Poirier (2016) Partial independence in nonseparable models self | 0.511 | 2 | 1 | 100% |
| 10 | Canay, I. A. and A. M. Shaikh (2017) Practical and theoretical advances in inference for partially identified models, in | 0.405 | 1 | 1 | 100% |
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