# Semiparametric Inference for Dynamic Causal Effects from Observational Time Series

arXiv: https://arxiv.org/abs/2609.27531 · HTML: https://mlsanity.com/paper/2609.27531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2026.

## Built on by (most cited first, 1 of 1)

- [Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation](https://mlsanity.com/paper/2610.05618/) (2026)

## Abstract (arXiv)

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.