CAUSAL INFERENCE, PATH ANALYSIS AND RECURSIVE STRUCTURAL EQUATIONS MODELS
نویسندگان
چکیده
منابع مشابه
Cause and Correlation in Biology A User’s Guide to Path Analysis, Structural Equations and Causal Inference
A catalogue record for this book is available from the British Library Library of Congress cataloguing in publication data Shipley, Bill, 1960– Cause and correlation in biology: a user's guide to path analysis, structural equations and causal inference / Bill Shipley. p. cm. ISBN 0 521 79153 7 (hb) 1. Biometry. I. Title.
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 کاملMarginal structural models and causal inference in epidemiology.
In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a m...
متن کاملStructural Inference With Long-run Recursive Empirical Models
This paper investigates conditions under which empirical models that use long-run recursive identifying assumptions will obtain structural impulse response functions. I present a class of structures defined as long-run partially recursive. If an economic structure falls into this class, then certain long-run recursive empirical models are able to identify some of the structural responses. This ...
متن کاملCausal 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ETS Research Report Series
سال: 1988
ISSN: 2330-8516
DOI: 10.1002/j.2330-8516.1988.tb00270.x