arXiv 1 Feb 2020 · Statistics — Methodology
arXiv:2002.00202 · PDF · Extracted main text
The term natural experiment is used inconsistently. In one interpretation, it refers to an experiment where a treatment is randomly assigned by someone other than the researcher. In another interpretation, it refers to a study in which there is no controlled random assignment, but treatment is assigned by some external factor in a way that loosely resembles a randomized experiment---often described as an "as if random" assignment. In yet another interpretation, it refers to any non-randomized study that compares a treatment to a control group, without any specific requirements on how the treatment is assigned. I introduce an alternative definition that seeks to clarify the integral features of natural experiments and at the same time distinguish them from randomized controlled experiments. I define a natural experiment as a research study where the treatment assignment mechanism (i) is neither designed nor implemented by the researcher, (ii) is unknown to the researcher, and (iii) is probabilistic by virtue of depending on an external factor. The main message of this definition is that the difference between a randomized controlled experiment and a natural experiment is not a matter of degree, but of essence, and thus conceptualizing a natural experiment as a research design akin to a randomized experiment is neither rigorous nor a useful guide to empirical analysis. Using my alternative definition, I discuss how a natural experiment differs from a traditional observational study, and offer practical recommendations for researchers who wish to use natural experiments to study causal effects.
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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 | Lassen (2005) The effect of information on voter turnout: Evidence from a natural experiment | 1.000 | 19 | 6 | 100% |
| 2 | Imbens and Rubin (2015) Causal inference in statistics, social, and biomedical sciences | 1.000 | 14 | 4 | 100% |
| 3 | Titiunik (2016) Drawing your senator from a jar: Term length and legislative behavior self | 1.000 | 7 | 4 | 100% |
| 4 | Deaton (2010) Instruments, randomization, and learning about development | 1.000 | 5 | 3 | 100% |
| 5 | Gerber and Green (2012) Field experiments: Design, analysis, and interpretation | 1.000 | 5 | 3 | 100% |
| 6 | Dunning (2012) Natural experiments in the social sciences: a design-based approach | 0.874 | 5 | 2 | 100% |
| 7 | Abadie and Cattaneo (2018) Econometric Methods for Program Evaluation | 0.843 | 3 | 3 | 100% |
| 8 | Angrist and Krueger (2001) Instrumental variables and the search for identification: From supply and demand to natural experiments | 0.843 | 3 | 3 | 100% |
| 9 | Dunning (2008) Improving causal inference: Strengths and limitations of natural experiments | 0.843 | 3 | 3 | 100% |
| 10 | Sekhon and Titiunik (2012) When natural experiments are neither natural nor experiments | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 65 scored citations.