On the Equivalence of Causal Models

03/27/2013
by   Tom S. Verma, et al.
0

Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data. Two causal models are equivalent if there is no experiment which could distinguish one from the other. A canonical representation for causal models is presented which yields an efficient graphical criterion for deciding equivalence, and provides a theoretical basis for extracting causal structures from empirical data. This representation is then extended to the more general case of an embedded causal model, that is, a dag in which only a subset of the variables are observable. The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information. This algorithm leads to a model theoretic definition of causation in terms of statistical dependencies.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8

01/18/2022

On the Equivalence of Causal Models: A Category-Theoretic Approach

We develop a category-theoretic criterion for determining the equivalenc...
06/02/2022

Causal Structure Learning: a Combinatorial Perspective

In this review, we discuss approaches for learning causal structure from...
01/30/2013

On the Semi-Markov Equivalence of Causal Models

The variability of structure in a finite Markov equivalence class of cau...
02/25/2021

A Local Method for Identifying Causal Relations under Markov Equivalence

Causality is important for designing interpretable and robust methods in...
03/13/2013

An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation

In a previous paper [Pearl and Verma, 1991] we presented an algorithm fo...
12/15/2018

Causal Identification under Markov Equivalence

Assessing the magnitude of cause-and-effect relations is one of the cent...
03/06/2013

Causal Modeling

Causal Models are like Dependency Graphs and Belief Nets in that they pr...