Causal Inference from Self-controlled Case Series
Abstract
The self-controlled case series (SCCS) design is widely used in studies of rare events, for example of vaccine side effects. Conceptually, the design has two main features. First, it requires only data from individuals who have experienced the event of interest. Therefore, the design is especially useful when the outcome is rare and the available data come from surveillance or clinical records, say, of hospitalized patients. Second, because comparisons are made within single individuals, time-invariant confounding between treatment and outcome is eliminated. We formulate a set of sufficient identification assumptions for estimating the causal effect of vaccination on an adverse outcome under the SCCS design, drawing on ideas from the causal inference literature. Unlike the existing SCCS literature, we do not consider a parametric model as the starting point. This allows us to establish when and why the SCCS design allows identification of causal effects, using data from cases only. However, because the sufficient identifiability assumptions are implausible in some applications, we also develop statistical tests for the existence of a treatment effect that remain valid when only a subset of the identification assumptions holds.
Related
- Semiparametric Inference for Dynamic Causal Effects from Observational Time Series
- Proximal Causal Inference for Time Series from N-of-1Mobile Device Data
- CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
- Longitudinal causal inference under informative missingness using the self-censoring model
- Whole-brain modeling of dynamic causal circuits in human cognition using amortized variational inference
BibTeX
@misc{perenyi2026causal,
title = {{Causal Inference from Self-controlled Case Series}},
author = {Gellért Perényi and Marco Piccininni and Mats J. Stensrud},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03531},
eprint = {2611.03531},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03531},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Causal Inference from Self-controlled Case Series
AU - Gellért Perényi
AU - Marco Piccininni
AU - Mats J. Stensrud
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03531
UR - https://arxiv.org/abs/2611.03531
AB - The self-controlled case series (SCCS) design is widely used in studies of rare events, for example of vaccine side effects. Conceptually, the design has two main features. First, it requires only data from individuals who have experienced the event of interest. Therefore, the design is especially useful when the outcome is rare and the available data come from surveillance or clinical records, say, of hospitalized patients. Second, because comparisons are made within single individuals, time-invariant confounding between treatment and outcome is eliminated. We formulate a set of sufficient identification assumptions for estimating the causal effect of vaccination on an adverse outcome under the SCCS design, drawing on ideas from the causal inference literature. Unlike the existing SCCS literature, we do not consider a parametric model as the starting point. This allows us to establish when and why the SCCS design allows identification of causal effects, using data from cases only. However, because the sufficient identifiability assumptions are implausible in some applications, we also develop statistical tests for the existence of a treatment effect that remain valid when only a subset of the identification assumptions holds.
ER -