Treatment Effects in Extreme Regimes
This addresses a critical issue for researchers and practitioners in causal inference and risk assessment, though it is incremental as it builds on existing extreme value theory methods.
The paper tackles the problem of estimating treatment effects in extreme regimes, where data is rare and counterfactuals are unavailable, by proposing a new framework based on extreme value theory that quantifies effects through variations in tail decay rates, and demonstrates efficacy on synthetic and semi-synthetic datasets.
Understanding treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the unavailability of counterfactual outcomes and the rarity and difficulty of collecting extreme data in practice. To address this issue, we propose a new framework based on extreme value theory for estimating treatment effects in extreme regimes. We quantify these effects using variations in tail decay rates of potential outcomes in the presence and absence of treatments. We establish algorithms for calculating these quantities and develop related theoretical results. We demonstrate the efficacy of our approach on various standard synthetic and semi-synthetic datasets.