Estimation of Treatment Effects in Extreme and Unobserved Data

Jiyuan Tan, Vasilis Syrgkanis, Jose Blanchet

Keywords: Causal Inference, Extreme Value Theory, Treatment Effect

TL;DR: We estimate average treatment effects, when the effect is moderated by extreme events and we are interested in the average effect in the extreme, which is rarely occurring in the observed data.

Abstract: 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.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 22721