2,621 research outputs found
Establishing Markov Equivalence in Cyclic Directed Graphs
We present a new, efficient procedure to establish Markov equivalence between
directed graphs that may or may not contain cycles under the
\textit{d}-separation criterion. It is based on the Cyclic Equivalence Theorem
(CET) in the seminal works on cyclic models by Thomas Richardson in the mid
'90s, but now rephrased from an ancestral perspective. The resulting
characterization leads to a procedure for establishing Markov equivalence
between graphs that no longer requires tests for d-separation, leading to a
significantly reduced algorithmic complexity. The conceptually simplified
characterization may help to reinvigorate theoretical research towards sound
and complete cyclic discovery in the presence of latent confounders. This
version includes a correction to rule (iv) in Theorem 1, and the subsequent
adjustment in part 2 of Algorithm 2.Comment: Correction to original version published at UAI-2023. Includes
additional experimental results and extended proof details in supplemen
Graphical modeling of stochastic processes driven by correlated errors
We study a class of graphs that represent local independence structures in
stochastic processes allowing for correlated error processes. Several graphs
may encode the same local independencies and we characterize such equivalence
classes of graphs. In the worst case, the number of conditions in our
characterizations grows superpolynomially as a function of the size of the node
set in the graph. We show that deciding Markov equivalence is coNP-complete
which suggests that our characterizations cannot be improved upon
substantially. We prove a global Markov property in the case of a multivariate
Ornstein-Uhlenbeck process which is driven by correlated Brownian motions.Comment: 43 page
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex
systems, their existence is ignored in a large part of the causal discovery
literature, as systems are typically assumed to be acyclic from the outset.
When applying causal discovery algorithms designed for the acyclic setting on
data generated by a system that involves feedback, one would not expect to
obtain correct results. In this work, we show that---surprisingly---the output
of the Fast Causal Inference (FCI) algorithm is correct if it is applied to
observational data generated by a system that involves feedback. More
specifically, we prove that for observational data generated by a simple and
-faithful Structural Causal Model (SCM), FCI is sound and complete, and
can be used to consistently estimate (i) the presence and absence of causal
relations, (ii) the presence and absence of direct causal relations, (iii) the
absence of confounders, and (iv) the absence of specific cycles in the causal
graph of the SCM. We extend these results to constraint-based causal discovery
algorithms that exploit certain forms of background knowledge, including the
causally sufficient setting (e.g., the PC algorithm) and the Joint Causal
Inference setting (e.g., the FCI-JCI algorithm).Comment: Major revision. To appear in Proceedings of the 36 th Conference on
Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 202
- …