Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems.
Robert Osazuwa NessKaushal PaneriOlga VitekPublished in: NeurIPS (2019)
Keyphrases
- complex systems
- markov processes
- random fields
- dynamic systems
- markov process
- continuous time bayesian networks
- markov chain
- stochastic processes
- non stationary
- multi agent systems
- probabilistic inference
- physical systems
- continuous time markov chains
- technical systems
- discrete event systems
- steady state simulation
- bayesian networks
- biological systems
- belief networks
- parameter estimation
- markov random field
- stochastic process
- bayesian inference
- conditional random fields
- reinforcement learning