Estimation of Treatment Effects in Extreme and Unobserved Data
This addresses a gap in causal inference for rare but impactful events, such as extreme climate events, which is incremental as it adapts existing theory to a specific domain.
The paper tackles the problem of estimating causal treatment effects for rare, extreme events where standard methods fail due to data scarcity, by introducing a novel framework based on Extreme Value Theory and multivariate regular variation, resulting in a consistent estimator with rigorous non-asymptotic performance analysis demonstrated on synthetic and semi-synthetic data.
Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. But what if we are interested in estimating the effects of a policy intervention whose benefits, while potentially important, can only be observed and measured in rare yet impactful events, such as extreme climate events? The standard causal inference methodology is not designed for this type of inference since the events of interest may be scarce in the observed data and some degree of extrapolation is necessary. Extreme Value Theory (EVT) provides methodologies for analyzing statistical phenomena in such extreme regimes. We introduce a novel framework for assessing treatment effects in extreme data to capture the causal effect at the occurrence of rare events of interest. In particular, we employ the theory of multivariate regular variation to model extremities. We develop a consistent estimator for extreme treatment effects and present a rigorous non-asymptotic analysis of its performance. We illustrate the performance of our estimator using both synthetic and semi-synthetic data.