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arXiv · 2609.30007

First as Tragedy? Second as What? Estimating Dynamic Effects of Recurrent Events

Agnes Norris Keiller
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Abstract

I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.

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Agnes Norris Keiller. 2026-09-24. First as Tragedy? Second as What? Estimating Dynamic Effects of Recurrent Events. https://arxiv.org/abs/2609.30007

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