Restricted Causal Inference Algorithm
نویسنده
چکیده
This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief network. Its correctness is demonstrated.
منابع مشابه
Causal Inference on Time Series using Restricted Structural Equation Models
Causal inference uses observational data to infer the causal structure of the data generating system. We study a class of restricted Structural Equation Models for time series that we call Time Series Models with Independent Noise (TiMINo). These models require independent residual time series, whereas traditional methods like Granger causality exploit the variance of residuals. This work conta...
متن کاملFast Restricted Causal Inference
Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm ”accelerating” the known CI algorithm of Spirtes, Glymour and Scheines [20]. We prove that this algorithm does not produces (conditional) independencies ...
متن کاملRestricted Structural Equation Models for Causal Inference
Causal inference tries to solve the following problem: given i.i.d. data from a joint distribution, one tries to infer the underlying causal DAG (directed acyclic graph), in which each node represents one of the observed variables. For approaching this problem, we have to make assumptions that connect the causal graph with the joint distribution. Independence-based methods like the PC algorithm...
متن کاملExperimental Learning of Causal Models with Latent Variables
This article discusses graphical models that can handle latent variables without explicitly modeling them quantitatively. There exist several paradigms for such problem domains. Two of them are semi-Markovian causal models and maximal ancestral graphs. Applying these techniques to a problem domain consists of several steps, typically: structure learning from observational and experimental data,...
متن کاملذخیره 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 ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1706.10117 شماره
صفحات -
تاریخ انتشار 2017