A Practical Approach to Causal Inference over Time
Authors: Martina Cinquini, Isacco Beretta, Salvatore Ruggieri, Isabel Valera
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we focus on estimating the causal effect of an intervention over time on a dynamical system. 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. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Pisa, Pisa, Italy 2Department of Computer Science, Saarland University, Saarbr ucken, Germany EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and prose but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/marti5ini/ci-over-time |
| Open Datasets | Yes | Datasets We rely on two synthetic datasets, German6 and Pendulum, and the real-world Census dataset7. German sim- 6This dataset is inspired on https://archive.ics.uci.edu/dataset/ 144/statlog+german+credit+data 7https://data.census.gov/ |
| Dataset Splits | No | The paper mentions using a "test set" for evaluation but does not specify the splitting percentages, absolute counts, or methodology for creating training, validation, and test splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Interventions We perform causal interventions on the root node Expertise and observe the effect on the target variable Credit Score. For the additive case, we apply F = 0.2, while for forcing, we use F = 1 with a target value of ˆE = 5. |