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...

In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial regression discontinuity design articles. We also find no (i) published in the top five journals different; (ii) “revise resubmit” process mitigates problem; (iii) things improving through time. Kranz Pütz...

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...