What to Observe When Assuming Selection on Observables
Bibliographic Data
| ID | 12370710 |
|---|---|
| Authors | Kevin M Quinn (0000-0003-0648-0220, Emory University, corresponding author), Guoer Liu (0000-0002-7309-9801, University of California San Diego), Lee Epstein (0000-0001-5120-9005, Washington University in St. Louis), A D Martins (0000-0002-6532-0721, Washington University in St. Louis), Andrew D Martin |
| Year | 2025 |
| Pages | 1-22 |
| Publication date | 2025-06-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/pan.2025.10003 |
| OpenAlex | W4411707785 |
| Language | EN |
| References cited | 33 |
Political scientists regularly rely on a selection-on-observables assumption to identify causal effects of interest. Once a causal effect has been identified in this way, a wide variety of estimators can, in principle, be used to consistently estimate the effect of interest. While these estimators are all justified by appeals to the same causal identification assumptions, they often differ greatly in how they make use of the data at hand. For instance, methods based on regression rely on an explicit model of the outcome variable but do not explicitly model the treatment assignment process, whereas methods based on propensity scores explicitly model the treatment assignment process but do not explicitly model the outcome variable. Understanding the tradeoffs between estimation methods is complicated by these seemingly fundamental differences. In this paper we seek to rectify this problem. We do so by clarifying how most estimators of causal effects that are justified by an appeal to a selection-on-observables assumption are all special cases of a general weighting estimator. We then explain how this commonality provides for diagnostics that allow for meaningful comparisons across estimation methods—even when the methods are seemingly very different. We illustrate these ideas with two applied examples
Econometrics · Mathematical economics · Observable · Physics · Quantum mechanics · Selection (genetic algorithm · Statistical physics · Statistics · Advanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Inference · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |