Dynamic and heterogeneous treatment effects with abrupt changes

TL;DR

This paper introduces a nonparametric, kernel-based method for detecting abrupt changes in treatment effects over time, accounting for heterogeneity and temporal dependence, with proven consistency and practical validation.

Contribution

It proposes a novel kernel-based change point detection approach for dynamic, heterogeneous treatment effects with theoretical guarantees and empirical validation.

Findings

Consistent change point estimator with controlled detection delay

Effective in detecting abrupt shifts in treatment effects

Validated through numerical experiments

Abstract

From personalised medicine to targeted advertising, it is an inherent task to provide a sequence of decisions with historical covariates and outcome data. This requires understanding of both the dynamics and heterogeneity of treatment effects. In this paper, we are concerned with detecting abrupt changes in the treatment effects in terms of the conditional average treatment effect (CATE) in a sequential fashion. To be more specific, at each time point, we consider a nonparametric model to allow for maximal flexibility and robustness. Along the time, we allow for temporal dependence on historical covariates and noise functions. We provide a kernel-based change point estimator, which is shown to be consistent in terms of its detection delay, under an average run length control. Numerical results are provided to support our theoretical findings.