Causal Discovery Under Non-Stationary Feedback

@inproceedings{Strobl2017CausalDU,
  title={Causal Discovery Under Non-Stationary Feedback},
  author={Eric A. Strobl},
  year={2017}
}
Causal discovery algorithms help investigators infer causal relations between random variables using observational data. In this thesis, I relax the acyclicity and stationary distribution assumptions imposed by the Fast Causal Inference (FCI) algorithm, a constraint-based causal discovery method allowing latent common causes and selection bias. I provide two major contributions in doing so. First, I introduce a representation of causal processes called Continuous time Markov processes with Jump… CONTINUE READING

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