This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (NeymanRubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper...

Actuarial risk assessments might be unduly perceived as a neutral way to counteract implicit bias and increase the fairness of decisions made at almost every juncture of the criminal justice system, from pretrial release to sentencing, parole and probation. In recent times these assessments have come under increased scrutiny, as critics claim that the statistical techniques underlying them migh...

In recent papers, Lee & Holyoak (2007, 2008a, 2008b) argue that extant models of analogy fail to explain how people draw inferences from causal analogies. In the current research, we argue that structure-mapping theory sufficiently explains the analogical inferences drawn from these causal analogies, and that, contrary to L&H‘s claims, the effect inference can indeed be evaluated by a post-anal...

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a ...

abstract the root and foot rot of wheat is one of the important disease of wheat over the world. the root and foot rot of wheat observed more frequently in the resent years at khorasan province (northeast of iran). for investigation the causal agents, several samples were collected from wheat fields of north khorasan in 2007 and 2008. culture of diseased parts of plants on pda resulted. fusariu...

We investigate how learning that an established type-level causal relationship is implemented by human agency affects people’s conceptualization of this relationship. In particular, we ask under what conditions subjects continue to perceive the original root cause as appropriate explanation for the resulting effect, and under what conditions they perceive the mediating intentional action as alt...

This paper deals with distributed control problems by means of distributed games played on Mazurkiewicz traces. The main difference with other notions of distributed games recently introduced is that, instead of having a local view, strategies and controllers are able to use a more accurate memory, based on their causal view. Our main result states that using the causal view makes the control s...

Instrumental Variables are a popular way to identify the direct causal effect of a random variable X on a variable Y . Often no single instrumental variable exists, although it is still possible to find a set of generalized instrumental variables (GIVs) and identify the causal effect of all these variables at once. Till now it was not known how to find GIVs systematically or even test efficient...

The purpose of this study is to investigate the causal relationship between the structure and performance in the banking industry of Iran. In doing so, the data and information of public and private banks from 1996 to 2015 is examined using Toda-Yamamoto causality test (TY) and Autoregressive Distributed Lag (ARDL) approach. Herfindahl-Hirschman index (HHI) (the degree of market concentration) ...

This paper addresses modeling causal relationships over event streams where data are unbounded and hence incremental modeling is required. There is no existing work for incremental causal modeling over event streams. Our approach is based on Popper’s three conditions which are generally accepted for inferring causality – temporal precedence of cause over effect, dependency between cause and eff...