MedicinemedRxiv
Ranked by formulaVerdict: NotableEstimating excess mortality during heat waves using a causal inference framework and non-randomized time series
Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome.
Key numbers
- 95% Fisherian intervals to be
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome. This is common in environmental epidemiology research, where the assignment to an exposure can be unethical or impossible. Consider a setting in which we seek to estimate excess mortality during heat waves, but are unable to randomize individuals to moderate or extreme heat. We propose to follow a causal framework for non-randomized time series that uses what we call historical time series matching to construct hypothetical randomized experiments, in which background covariates are balanced. We applied this framework and assessed the following causal question: Are daily mortality counts among elderly Americans higher during hot versus warm summer time periods? We addressed this causal question by drawing frequentist, Fisherian, and Bayesian inferences for five American cities. We estimated the average treatment effect of heat on three-day mortality with 95% Fisherian intervals to be 0.5 [-3.6, 4.4] deaths in Pittsburgh, 3.7 [0.7, 6.6] deaths in Seattle, 12.5 [6.1, 18.9] deaths in Chicago, 8.5 [1.4, 15.6] deaths in Los Angeles, and 20.3 [11.2, 29.5] deaths in New York.
The editor's rubric
| Dimension | Level | Weight | What that level means |
|---|---|---|---|
| Leverage | ███░░ 3 | 10% | A method or resource many groups across the field will adopt within a year. |
| Magnitude | ██░░░ 2 | 20% | Solid incremental gain on a meaningful problem. |
| Evidence | ███░░ 3 | 32% | Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data. |
| Novelty | ██░░░ 2 | 8% | A new combination of known ideas. |
| Trajectory | ██░░░ 2 | 5% | Some room to improve with obvious engineering. |
| Stakes | ███░░ 3 | 25% | Meaningful benefit to many people within a few years. |
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); design (randomized); stakes (mortality, prevention or cure).
How the score was computed
- Merit
- 5.3 / 10
- Adjusted merit
- 4.5 / 10
- Attention
- 0%
- Freshness
- 92%
- Citations0 (reference 20, via semantic-scholar, Oct 15, 2026, 06:17 UTC)