nowadays we know effective supply chain management a key to business success. therefore, supply chain managers use many practices for scm effectiveness and many tools as their enablers. nevertheless, literature about causal relations between sc enablers and scm practices and performance is scarce. this study reports a cognitive mapping of causal relationships between scm practices, sc enablers ...

conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  as  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itsel...

One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest “weak” pa...

In this paper we argue that the development of knowledgebased systems built to work in partially uncertain domains bene t from the use of di erent conceptualisations for certain and uncertain parts of the knowledge. We present conceptualisations that have proven to be useful, namely the KADS model of expertise and a causal model of uncertainty that re ects well known approaches to uncertain rea...

Let me emphasize at the outset that as the terms are being used here, causal inference is not the same as statistical inference. The two types of inference are similar in that they both use “localized” information to draw conclusions about more general phenomena; however the types of phenomena about which one seeks to generalize are not the same and the types of information used also often diff...

The causal Markov condition (CMC) plays an important role in much recent work on the problem of causal inference from statistical data. It is commonly thought that the CMC is a more problematic assumption for genuinely indeterministic systems than for deterministic ones. In this essay, I critically examine this proposition. I show how the usual motivation for the CMC—that it is true of any acyc...

This paper argues that the current way in which the undergraduate introductory econometrics course is taught is neither inline with current empirical practice nor very intuitive. It proposes a shift in focus of the course on causal inference using the Roy-Rubin Causal Model (RRCM). A second theme of the paper is the suggestion to use random regressors from the start to improve the ability of st...

We propose a framework for general-purpose imitation learning centered on cause-effect reasoning. Our approach infers a hierarchical representation of a demonstrator’s intentions, which can explain why they acted as they did. This enables rapid generalization of the observed actions to new situations. We employ a novel causal inference algorithm with formal guarantees and connections to automat...