Optimizing Causal Orderings for Generating DAGs from Data

نویسنده

  • Remco R. Bouckaert
چکیده

An algorithm for generating the structure of a directed acyclic graph from data using the notion of causal input lists is prest'nted. The algorithm manipulates the ordt'ring of the variables with operations which very much resemble arc reversal. Operations are only applied if the DAG after the operation repre­ sents at least the independencies represented by the DAG before the operation until no more arcs can be removed from the DAG. The resulting DAG is a minimal 1-map.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data

Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of gene-expression experiments involving observation as well as, usually few, interventions, like knock-outs. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the fu...

متن کامل

Joint Probabilistic Inference of Causal Structure

Causal directed acyclic graphical models (DAGs) are powerful reasoning tools in the study and estimation of cause and effect in scientific and socio-behavioral phenomena. In many domains where the cause and effect structure is unknown, a key challenge in studying causality with DAGs is learning the structure of causal graphs directly from observational data. Traditional approaches to causal str...

متن کامل

4 Conclusions and Further Research

9] S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities on graphical structures and their applications to expert systems (with discussion). 13 algorithms currently known for inference in probabilistic networks. IDAGs can also be helpful for constructing a DAG from a given independency model. For the construction of a DAG from an independency model, in 1] an algorithm has...

متن کامل

Graphical Causal Models

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto t...

متن کامل

Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause-effect fashion. How does such immediate causation, tha...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1992