Necessary and Sufficient Conditions are Converse Relations

According to the so-called ‘standard theory’ of conditions, the conditionship relation is converse, that is, if A is a sufficient condition for B, B is a necessary condition for A. This theory faces well-known counterexamples that appeal to both causal and other asymmetric considerations. I show that these counterexamples lose their plausibility once we clarify two key components of the standard theory: that to satisfy a condition is to instantiate a property, and that what is usually called ‘conditionship relation’ is an inferential relation. Throughout the paper this way of interpreting the standard theory is compared favourably over an alternative interpretation that is outlined in causal terms, since it can be applied to all counterexamples without losing its intuitive appeal

Counterfactual Causality from First Principles?

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277

Generating Diagnoses for Probabilistic Model Checking Using Causality

One of the most major advantages of Model checking over other formal methods of verification, its ability to generate an error trace in case of a specification falsified in the model. We call this trace a counterexample. However, understanding the counterexample is not that easy task, because model checker generates usually multiple counterexamples of long length, what makes the analysis of counterexample time-consuming as well as costly task. Therefore, counterexamples should be small and as indicative as possible to be understood. In probabilistic model checking (PMC) counterexample generation has a quantitative aspect.  The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability bound. In this paper, we address the complementary task of counterexample generation which is the counterexample diagnosis in PMC. We propose an aided-diagnostic method for probabilistic counterexamples based on the notion of causality and responsibility. Given a counterexample for a Probabilistic CTL (PCTL) formula that doesn’t hold over Discreet-Time-Markov-Chain (DTMC) model, this method guides the user to the most responsible causes in the counterexample.</p

Visual counterexample explanation for model checking with Oeritte

Despite being one of the most reliable approaches for ensuring system correctness, model checking requires auxiliary tools to fully avail. In this work, we tackle the issue of its results being hard to interpret and present Oeritte, a tool for automatic visual counterexample explanation for function block diagrams. To learn what went wrong, the user can inspect a parse tree of the violated LTL formula and a table view of a counterexample, where important variables are highlighted. Then, on the function block diagram of the system under verification, they can receive a visualization of causality relationships between the calculated values of interest and intermediate results or inputs of the function block diagram. Thus, Oeritte serves to decrease formal model and specification debugging efforts along with making model checking more utilizable for complex industrial systems.Comment: The 25th International Conference on Engineering of Complex Computer Systems (ICECCS 2020

Sensitivity, safety, and the law: A reply to Pardo

ABSTRACTIn a recent paper, Michael Pardo argues that the epistemic property that is legally relevant is the one called Safety, rather than Sensitivity. In the process, he argues against our Sensitivity-related account of statistical evidence. Here we revisit these issues, partly in order to respond to Pardo, and partly in order to make general claims about legal epistemology. We clarify our account, we show how it adequately deals with counterexamples and other worries, we raise suspicions about Safety's value here, and we revisit our general skepticism about the role that epistemological considerations should play in determining legal policy

Manipulationism, Ceteris Paribus Laws, and the Bugbear of Background Knowledge

According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibili-ty. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generali-sations, and these are not well adapted to encoding such knowledge, especially in the social sciences, as some of it is non-causal. Ceteris paribus (CP) laws or generalisa-tions labour under no such difficulty. A survey of research methods such as case and comparative studies, randomised control trials, ethnography, and structural equation modeling, suggests that it would be more difficult and in some instances impossible to try to represent the output of each method in invariant generalisations; and that this is because in each method causal and non-causal background knowledge mesh in a way that cannot easily be accounted for in manipulationist terms. Ceteris paribus-generalisations being superior in this regard, a theory of explanation based on the latter is a better fit for social science

A new proposal how to handle counterexamples to Markov causation à la Cartwright, or: fixing the chemical factory

Cartwright (Synthese 121(1/2):3-27, 1999a; The dappled world, Cambridge University Press, Cambridge, 1999b) attacked the view that causal relations conform to the Markov condition by providing a counterexample in which a common cause does not screen off its effects: the prominent chemical factory. In this paper we suggest a new way to handle counterexamples to Markov causation such as the chemical factory. We argue that Cartwright's as well as similar scenarios (such as decay processes, EPR/B experiments, or spontaneous macro breaking processes) feature a certain kind of non-causal dependence that kicks in once the common cause occurs. We then develop a representation of this specific kind of non-causal dependence that allows for modeling the problematic scenarios in such a way that the Markov condition is not violated anymore

‘Pain Always Asks for a Cause’: Nietzsche and Explanation

© 2017 John Wiley & Sons Ltd Those who have emphasised Nietzsche's naturalism have often claimed that he emulates natural scientific methods by offering causal explanations of psychological, social, and moral phenomena. In order to render Nietzsche's method consistent with his methodology, such readers of Nietzsche have also claimed that his objections to the use of causal explanations are based on a limited scepticism concerning the veracity of causal explanations. My contention is that proponents of this reading are wrong about both Nietzsche's methodology and his method. I argue for this by: first, showing that Nietzsche was suspicious of causal explanations not only on sceptical grounds but also for reasons provided by his psychological analysis of our tendency to look for causes; and second, arguing for a non-causal interpretation of Nietzsche's approach to psychological explanation