# Using Time Structure to Estimate Causal Effects

arXiv: https://arxiv.org/abs/2504.11076 · HTML: https://mlsanity.com/paper/2504.11076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past](https://mlsanity.com/paper/2203.06056/) (2022)
- [Characterization of causal ancestral graphs for time series with latent confounders](https://mlsanity.com/paper/2112.08417/) (2021)
- [DecoR: Deconfounding Time Series with Robust Regression](https://mlsanity.com/paper/2406.07005/) (2024)
- [Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs](https://mlsanity.com/paper/2409.15530/) (2024)

## Abstract (arXiv)

There exist several approaches for estimating causal effects in time series when latent confounding is present. Many of these approaches rely on additional auxiliary observed variables or time series such as instruments, negative controls or time series that satisfy the front- or backdoor criterion in certain graphs. In this paper, we present a novel approach for estimating direct (and via Wright's path rule total) causal effects in a time series setup which does not rely on additional auxiliary observed variables or time series. This approach assumes that the underlying time series is a Structural Vector Autoregressive (SVAR) process and estimates direct causal effects by solving certain linear equation systems made up of different covariances and model parameters. We state sufficient graphical criteria in terms of the so-called full time graph under which these linear equations systems are uniquely solvable and under which their solutions contain the to-be-identified direct causal effects as components. We also state sufficient lag-based criteria under which the previously mentioned graphical conditions are satisfied and, thus, under which direct causal effects are identifiable. Several numerical experiments underline the correctness and applicability of our results.