Semiparametric Inference for Dynamic Causal Effects from Observational Time Series

2026-09-23

AI Summary

研究解决了时间序列中动态因果效应推断问题，通过结合去偏机器学习和工具变量的半参数框架来处理高维预处理信息、未测量混淆因素及序列依赖。

Abstract

In observational time series, statistical inference for dynamic causal effects of a one-time intervention across horizons is complicated by high-dimensional observed pre-treatment information, unmeasured confounding, and serial dependence. To address these challenges, we develop a semiparametric framework for inference from a single serially dependent time series, integrating debiased machine learning with instrumental variables through buffered block cross-fitting. Under geometric beta-mixing, we derive non-asymptotic bounds on estimation error, asymptotic normality at each fixed horizon, and feasible inference that accommodates serial dependence. We further show how learner-specific prediction guarantees under temporal dependence can be used to verify the nuisance-rate conditions required for orthogonal inference. In a monetary-policy application with 468 months and 1464 lagged FRED-MD controls, we show an instrumented policy tightening lowers housing starts at medium horizons, with sensitivity analyses that support the finding.

Problem

• observational time series
• dynamic causal effects
• unmeasured confounding
• serial dependence

Innovation

• semiparametric inference
• debiased machine learning
• instrumental variables
• buffered block cross-fitting
• serial dependence