# Identifiability and Transportability in Dynamic Causal Networks

arXiv: https://arxiv.org/abs/1610.05556 · HTML: https://mlsanity.com/paper/1610.05556/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [Identifiability of total effects from abstractions of time series causal graphs](https://mlsanity.com/paper/2310.14691/) (2023)
- [Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion](https://mlsanity.com/paper/2406.05805/) (2024)
- [Complete Characterization for Adjustment in Summary Causal Graphs of Time Series](https://mlsanity.com/paper/2506.14534/) (2025)
- [Grasping Causality for the Explanation of Criticality for Automated Driving](https://mlsanity.com/paper/2210.15375/) (2022)
- [Learning Causal Models of Autonomous Agents using Interventions](https://mlsanity.com/paper/2108.09586/) (2021)
- [Causal Identification in Time Series Models](https://mlsanity.com/paper/2504.20172/) (2025)
- [Teleological Inference in Structural Causal Models via Intentional Interventions](https://mlsanity.com/paper/2603.18968/) (2026)

## Abstract (arXiv)

In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Net- works, namely, for computing the effect of an intervention or experiment, based on passive observations only, whenever possible. We note the existence of two types of confounder variables that affect in substantially different ways the iden- tification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs. We further propose a procedure for the transportability of causal effects in Dynamic Causal Network settings, where the re- sult of causal experiments in a source domain may be used for the identification of causal effects in a target domain.