# TranCIT: Transient Causal Interaction Toolbox

arXiv: https://arxiv.org/abs/2509.00602 · HTML: https://mlsanity.com/paper/2509.00602/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 30 Aug 2025.

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

- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [From univariate to multivariate coupling between continuous signals and point processes: a mathematical framework](https://mlsanity.com/paper/2005.04034/) (2020)
- [Brain as a complex system, harnessing systems neuroscience tools & notions for an empirical approach](https://mlsanity.com/paper/2312.13478/) (2023)
- [Information Theoretic Measures of Causal Influences during Transient Neural Events](https://mlsanity.com/paper/2209.07508/) (2022)

## Abstract (arXiv)

Quantifying transient causal interactions from non-stationary neural signals is a fundamental challenge in neuroscience. Traditional methods are often inadequate for brief neural events, and advanced, event-specific techniques have lacked accessible implementations within the Python ecosystem. Here, we introduce trancit (Transient Causal Interaction Toolbox), an open-source Python package designed to bridge this gap. TranCIT implements a comprehensive analysis pipeline, including Granger Causality, Transfer Entropy, and the more robust Structural Causal Model-based Dynamic Causal Strength (DCS) and relative Dynamic Causal Strength (rDCS) for accurately detecting event-driven causal effects. We demonstrate TranCIT's utility by successfully capturing causality in high-synchrony regimes where traditional methods fail and by identifying the known transient information flow from hippocampal CA3 to CA1 during sharp-wave ripple events in real-world data. The package offers a user-friendly, validated solution for investigating the transient causal dynamics that govern complex systems.