The XAI project contributes innovative research presented at AAAI 2025 on the topic of causal inference applied to dynamic systems over time.
The paper (Cinquini et al., 2025) formally introduces causal interventions and their effects on discrete-time stochastic processes (DSP). The research shows under which conditions the equilibrium states of a DSP, both before and after a causal intervention, can be captured by a structural causal model (SCM).
The proposed framework explicitly maps vector autoregressive models (VAR), widely applied in econometrics, to linear but potentially cyclic SCMs and/or affected by unmeasured confounders. This allows performing causal inference over time from observational time series data.
References
2025
A Practical Approach to Causal Inference over Time
Martina
Cinquini, Isacco
Beretta, Salvatore
Ruggieri, and Isabel
Valera
Proceedings of the AAAI Conference on Artificial Intelligence, Apr 2025
In this paper, we focus on estimating the causal effect of an intervention over time on a dynamical system.
To that end, we formally define causal interventions and their effects over time on discrete-time stochastic processes (DSPs).
Then, we show under which conditions the equilibrium states of a DSP, both before and after a causal intervention, can be captured by a structural causal model (SCM).
With such an equivalence at hand, we provide an explicit mapping from vector autoregressive models (VARs), broadly applied in econometrics, to linear, but potentially cyclic and/or affected by unmeasured confounders, SCMs. The resulting causal VAR framework allows us to perform causal inference over time from observational time series data.
Our experiments on synthetic and real-world datasets show that the proposed framework achieves strong performance in terms of observational forecasting while enabling accurate estimation of the causal effect of interventions on dynamical systems.
We demonstrate, through a case study, the potential practical questions that can be addressed using the proposed causal VAR framework.
@article{CBR2025,author={Cinquini, Martina and Beretta, Isacco and Ruggieri, Salvatore and Valera, Isabel},doi={10.1609/aaai.v39i14.33626},issn={2159-5399},journal={Proceedings of the AAAI Conference on Artificial Intelligence},line={2},month=apr,number={14},open_access={Gold},pages={14832–14839},publisher={Association for the Advancement of Artificial Intelligence (AAAI)},title={A Practical Approach to Causal Inference over Time},visible_on_website={YES},volume={39},year={2025}}
