# Causal Discovery Under Non-Stationary Feedback

@inproceedings{Strobl2017CausalDU, title={Causal Discovery Under Non-Stationary Feedback}, author={Eric A. Strobl}, year={2017} }

- Published 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

#### Citations

##### Publications citing this paper.

SHOWING 1-2 OF 2 CITATIONS

## A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias

VIEW 10 EXCERPTS

CITES BACKGROUND

HIGHLY INFLUENCED

## Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs

VIEW 3 EXCERPTS

CITES METHODS

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 58 REFERENCES

## Learning high-dimensional directed acyclic graphs with latent and selection variables

VIEW 10 EXCERPTS

HIGHLY INFLUENTIAL

## Causation, prediction, and search

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Restructuring Dynamic Causal Systems in Equilibrium

VIEW 3 EXCERPTS

HIGHLY INFLUENTIAL

## Learning Continuous Time Bayesian Networks

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## A Discovery Algorithm for Directed Cyclic Graphs

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Independence properties of directed markov fields

VIEW 2 EXCERPTS

HIGHLY INFLUENTIAL

## Estimating Mixtures of Normal Distributions and Switching Regressions

VIEW 1 EXCERPT

HIGHLY INFLUENTIAL

## Learning Causal Structures Using Regression Invariance

VIEW 1 EXCERPT