TY - JOUR
AU - Wahl, Jonas
AU - Ninad, Urmi
AU - Runge, Jakob
PY - 2023/06/26
Y2 - 2024/09/14
TI - Vector Causal Inference between Two Groups of Variables
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 37
IS - 10
SE - AAAI Technical Track on Reasoning Under Uncertainty
DO - 10.1609/aaai.v37i10.26450
UR - https://ojs.aaai.org/index.php/AAAI/article/view/26450
SP - 12305-12312
AB - Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or groups of scalar variables.We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. Our method employs sparsity estimates of directed and undirected graphs and is based on two new principles for groupwise causal reasoning that we justify theoretically in Pearl's graphical model-based causality framework. Our theoretical considerations are complemented by two new causal discovery algorithms for causal interactions between two random vectors which find the correct causal direction reliably in simulations even if interactions are nonlinear. We evaluate our methods empirically and compare them to other state-of-the-art techniques.
ER -