TY - GEN
T1 - Identifying nonlinear 1-step causal influences in presence of latent variables
AU - Salehkaleybar, Saber
AU - Etesami, Jalal
AU - Kiyavash, Negar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/9
Y1 - 2017/8/9
N2 - We propose an approach for learning the causal structure in stochastic dynamical systems with a 1-step functional dependency in the presence of latent variables. We propose an information-theoretic approach that allows us to recover the causal relations among the observed variables as long as the latent variables evolve without exogenous noise. We further propose an efficient learning method based on linear regression for the special sub-case when the dynamics are restricted to be linear. We validate the performance of our approach via numerical simulations.
AB - We propose an approach for learning the causal structure in stochastic dynamical systems with a 1-step functional dependency in the presence of latent variables. We propose an information-theoretic approach that allows us to recover the causal relations among the observed variables as long as the latent variables evolve without exogenous noise. We further propose an efficient learning method based on linear regression for the special sub-case when the dynamics are restricted to be linear. We validate the performance of our approach via numerical simulations.
UR - https://www.scopus.com/pages/publications/85034114905
U2 - 10.1109/ISIT.2017.8006747
DO - 10.1109/ISIT.2017.8006747
M3 - Conference contribution
AN - SCOPUS:85034114905
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1341
EP - 1345
BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Symposium on Information Theory, ISIT 2017
Y2 - 25 June 2017 through 30 June 2017
ER -