Yarkoni argues that one solution is to abandon quantitative methods for qualitative ones. While we agree that qualitative methods are undervalued, we argue that both are necessary for thoroughgoing psychological research, complementing one another through the use of causal analysis. We illustrate how directed acyclic graphs can bridge qualitative and quantitative methods, thereby fostering understanding between different psychological methodologies.

As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework to address when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and "replaced by more well-behaved notions". As harm is generally something that (...) is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality (Halpern, 2016). The key novelty of our definition is that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems. (shrink)

A naturalistic theory of rationalization is defended against a fundamental objection. The theory claims that: The rationalizing relation can be fully analysed in causal explanatory terms. However, is rendered problematic by the fact that: Rationalizations exhibit a higher degree of intensionality than ordinary physical causal explanations. To show that can be maintained in the face of , I develop an account of on which and may be reconciled. ;The opening chapter gives an account of the intensionality of ordinary (...) physical causal explanations by developing a counterfactual analysis of causal relevance. Chapter II examines the nature and intensionality of rationalizations. It is argued that despite , a rationalization partly involves a causal relevance relation between its explanans and explanandum. ;In Chapter III, I consider an argument which implies that is false: Davidson's argument that there can be no psychophysical laws. An attempt is made to give a clear and compelling representation of this argument. As I interpret it, the argument depends on the alleged insolubility of the problem of causal deviance in action. ;Chapter IV develops a general theory of deviance according to which any functional system may produce output in a deviant way, and applies this theory to agents. A negative consequence of the theory is that an action may be performed in a deviant way even if the agent's intention causally explains the action . A positive consequence of the theory is that an action is performed in a non-deviant way just in case there is an appropriate causal explanation of why the agent's intention causes him to act. My specific suggestion is that an agent performs an action in a non-deviant way when and only when his intention causes him to act because he "knows how" to act in that way. ;In the last chapter, it is argued that our solution to the problem of deviance can be adapted to account for in causal explanatory terms. In this way and are reconciled. (shrink)

In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only 'closed' and 'hermetic' systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be compatible and whether causal (...) class='Hi'>analysis can be integrated into the broader framework of system analysis. This article attempts a negative answer on the grounds of fundamental differences in their assumptions and suggests using system analysis as a post-hoc comparative tool. (shrink)

The total electron content in the ionosphere widely influences Global Navigation Satellite Systems especially for critical applications by inducing localized positional errors in the GNSS measurements. These errors can be mitigated by measuring TEC from stations located around the world at various temporal and spatial scales and using them for advanced forecasting of TEC. The TEC can be used as a tool in understanding space weather phenomena such as geomagnetic storms which cause disruptions in the ionosphere. This paper examines the (...) causal relationship between perturbations in TEC caused by geomagnetic storms. The causality between two geomagnetic indices auroral electrojet and disturbed storm index and TEC is investigated using Granger causality at two low-latitude stations, Bangalore and Hyderabad. The outcomes of this study strengthen the regional understanding and modeling of ionospheric parameters which can contribute towards the global efforts for modeling and reducing the ionospheric effects on trans-ionospheric communication and navigation. The causal inferences combined with the data-driven model can be useful in identifying the correct and informative physical quantities to improve the forecasting models. (shrink)

The retreat from the paradise of deterministic causation and the general principles involved in this retreat, which has been forced upon us by quantum mechanics, is described in more or less successive stages.

Contemporary analytical philosophy has not provided historians with an adequate account of their causal reasoning. Attempts to apply the laws of scientific explanation to history have occasioned an artificial split between historical interpretation and historical explanation. The lawlike generalizations of the natural sciences are both perfectly universal and perfectly delimited, whereas the typical generalizations of the historian are imperfectly universal and imperfectly delimited. In historical analysis, a particular development is hypothetically posited as the ordinary course of events, or (...) as the established "trend," and an intervening process or set of conditions is identified as the cause of some alteration in the expected outcome. It is important to differentiate between historical events and the actions of historical figures. The analysis of historical actions requires attention to a second layer of interpretation, that of intention. In a good narrative, everything "important" for the actual outcome will be clearly displayed and cogently ordered in a network of interacting causal sequences; for that is what the historian means when he proposes to "tell the whole story.". (shrink)

Discussions of the causal status of race focus on the question of whether race itself can be experimentally manipulated. Yet many experiments testing for racial discrimination do not manipulate race, but rather a signal by which race influences an outcome. Such signal manipulations are easily formalized, though contexts of discrimination introduce significant philosophical complications. Whether a signal counts as a signal for race is not merely a causal question, but depends on sociological and normative issues regarding discrimination. The (...) notion of signal manipulation enables one to take these issues into account while still using causal counterfactual tests to detect discrimination. The analysis provided here is compatible with social constructivism and helps differentiate between cases in which it is more or less fruitful to model race causally. (shrink)

Max Weber's methodological writings offered a model of singular causal analysis that anticipated key elements of contemporary Anglo-American philosophy of the social and cultural sciences. The model accurately portrayed crucial steps and dimensions of causal reasoning in these disciplines, outlining a dynamic and probabilistic conception of historical processes, counterfactual reasoning, and comparison as a substitute for counterfactual argument. Above all, Weber recognized the interpretation of human actions as a subcategory of causal analysis, in which the (...) agents' visions of desired outcomes, together with their beliefs about how to bring them about, cause them to act as they do. (shrink)

Many political philosophers hold the Feasible Alternatives Principle (FAP): justice demands that we implement some reform of international institutions P only if P is feasible and P improves upon the status quo from the standpoint of justice. The FAP implies that any argument for a moral requirement to implement P must incorporate claims whose content pertains to the causal processes that explain the current state of affairs. Yet, philosophers routinely neglect the need to attend to actual causal processes. (...) This undermines their arguments concerning moral requirements to reform international institutions. The upshot is that philosophers’ arguments must engage in causal analysis to a greater extent than is typical. -/- [Supplement: Handout available at http://db.tt/fyuVW3Xv]. (shrink)

We employ a frequency-dependent asymmetric and causality analysis to investigate the connectedness between gold and cryptocurrencies during the COVID-19 pandemic. Hence, the variational mode decomposition-based quantile regression is utilised. Findings from the study divulge that the variational mode functions at the lower quantiles are mostly significant and negative indicating that gold acts as a safe haven, a diversifier at most market conditions with insignificant coefficients, and a hedge at normal market conditions for most cryptocurrencies at various investment horizons. Particularly, (...) hedging benefits mostly occur in the short- and medium-term for Bitcoin and Ripple, as well as Bitcoin and Dogecoin in the long-term with gold. This implies that there is high persistence in the hedging properties of gold with Bitcoin, followed by Ripple. We notice more significant relationship between gold and some cryptocurrencies in the long-term of the COVID-19 pandemic relative to the medium-term emphasising the delayed responses of prices to information. Investors are recommended to be observant and mindful of investing in these markets due to the different dynamics. (shrink)

Thought experiments are ubiquitous in science and especially prominent in domains in which experimental and observational evidence is scarce. One such domain is the causal analysis of singular events in history. A long‐standing tradition that goes back to Max Weber addresses the issue by means of ‘what‐if’ counterfactuals. In this paper I give a descriptive account of this widely used method and argue that historians following it examine difference makers rather than causes in the philosopher’s sense. While difference (...) making is neither necessary nor sufficient for causation, to establish difference makers is more consistent with the historians’ more ultimate purposes. †To contact the author, please write to: Department of Philosophy, Erasmus University, 3000 DR Rotterdam, The Netherlands; e‐mail: [email protected]. (shrink)

World political processes, such as wars and globalisation, are engendered by complex sets of causes and conditions. Although the idea of causation is fundamental to the field of International Relations, what the concept of cause means or entails has remained an unresolved and contested matter. In recent decades ferocious debates have surrounded the idea of causal analysis, some scholars even questioning the legitimacy of applying the notion of cause in the study of International Relations. This book suggests that (...) underlying the debates on causation in the field of International Relations is a set of problematic assumptions and that we should reclaim causal analysis from the dominant discourse of causation. Milja Kurki argues that reinterpreting the meaning, aims and methods of social scientific causal analysis opens up multi-causal and methodologically pluralist avenues for future International Relations scholarship. (shrink)

By definition, metacognitive processes may monitor or regulate various stages of first-order processing. By combining causal analysis with hypotheses expressed by other authors we derive the theoretical and methodological consequences of this special relation between metacognition and the underlying processes. In particular, we prove that because multiple processing stages may be monitored or regulated and because metacognition may form latent feedback loops, (1) without strong additional causal assumptions, typical measures of metacognitive monitoring or regulation are confounded; (2) (...) without strong additional causal assumptions, typical methods of controlling for first-order task performance (i.e., calibration, staircase, including first-order task performance in a regression analysis, or analyzing correct and incorrect trials separately) not only do not deconfound measures of metacognition but may even introduce bias; (3) that the first two problems cannot be solved by using simple models of decision-making derived from Signal Detection Theory. We conclude the paper by advocating robust methods of discovering properties of latent mechanisms. (shrink)

The interventionist theory of causation has been advertised as an empirically informed and more nuanced approach to causality than the competing theories. However, previous literature has not yet analyzed the regression discontinuity (hereafter, RD) and the difference-in-differences (hereafter, DD) within an interventionist framework. In this paper, I point out several drawbacks of using the interventionist methodology for justifying the DD and RD designs. However, I argue that the first step towards enhancing our understanding of the DD and RD designs from (...) an interventionist perspective is to take advantage of the assumptions of common trend and continuity. (shrink)

Analyses of factual causation face perennial problems, including preemption, overdetermination, and omissions. Arguably, the thorniest, are cases of omissive overdetermination, involving two independent omissions, each sufficient for the harm, and neither, independently, making a difference. A famous example is Saunders, where pedestrian was hit by a driver of a rental car who never pressed on the (unbeknownst to the driver) defective (and, negligently, never inspected) brakes. Causal intuitions in such cases are messy, reflected in disagreement about which omission mattered. (...) What these analyses mistakenly take for granted, is that at issue is the 'efficacy' of each omission. I argue, on the contrary, the puzzle of omissive overdetermination favors taking the act/omission distinction seriously. Factual causation, properly understood precludes omissions (i.e. omissions are not causal). Of course, the law also attaches liability to omissions, but this works differently from liability for real causes (e.g. omissions have a duty requirement, they also respond differentially to difference-making considerations). The manner in which liability attaches for omissions differs from that of straightforward causal liability, and is entirely dependent on the underlying causal structure. Attention to that structure (e.g. that the driver's hitting the pedestrian with his car is what actually caused the injury) sheds light on which omissions matter (e.g. driver's failure to press on the brakes) and why (because that failure removes a defense the driver would have to liability for the accident he caused). Other cases, where the parties' connection is entirely omissive (e.g. two physicians fail to detect independently lethal conditions), come out differently (tracking moralized elements). The analysis offered makes better sense of both why omissive determination cases are puzzling and how to resolve them. (shrink)

Objects have dispositions. Dispositions are normally analyzed by providing a meaning to disposition ascriptions like ‘This piece of salt is soluble’. Philosophers like Carnap, Goodman, Quine, Lewis and many others have proposed analyses of such disposition ascriptions. In this paper we will argue with Quine that the proper analysis of ascriptions of the form ‘x is disposed to m ’, where ‘x’ denotes an object, ‘m’ a manifestation, and ‘C’ a condition, goes like this: ‘x is of natural kind (...) k’, and the generic ‘ks are m ’ is true. For the analysis of the generic, we propose an analysis in terms of causal powers: ‘ks have the causal power to m’. The latter, in turn, is analyzed in a very precise way, making use of Pearl’s probabilistic graphical causal models. We will show how this natural kind-analysis improves on standard conditional analyses of dispositions by avoiding the standard counterexamples, and that it gives rise to precise observable criteria under which the disposition ascription is true. (shrink)

In educational research, the ‘paradigm wars’—vehement debates between proponents of qualitative approaches and proponents of quantitative approaches—are far from over (Bryman 2006). The development of British educational research demonstrates this nicely. Influenced by policy makers’ demands for prediction and control, the current research climate is characterised by a mistrust of qualitative methods, particularly in regard to their potential value-ladenness and lack of rigour (Hodkinson 2008).British education sociologist and methodologist Martyn Hammersley, one of the authors of Challenging the Qualitative–Quantitative Divide: Explorations (...) in Case-Focused Causal Analysis, objects to the dichotomy between qualitative and quantitative methods that the paradigm wars are based on. He argues that the division is artificial and may even be damaging: for instance, it could discourage researchers of one approach from taking into account arguments produced by researchers of a different style .. (shrink)

This article analyzes Toole’s novel from a causal perspective, focusing on the cause-effect dynamics that make the plot advance, from the initial event at D.H. Holmes until the outcome in the Night of Joy. A combination of qualitative and quantitative methodologies has been applied to identify a series of 47 causal events that summarize all actions with an impact on plot development. Our research shows that the causal study of the novel is a useful approach that can (...) reinforce or modify qualitative assessments of previous studies. The relevance of “Night of Joy” plot within the whole narrative and its key role in the rest of the subplots are verified in numerical terms. Besides, we have expanded and quantified the causal chains operating in the novel; and we have made sure, as well, that every subplot is essential in order to reach the final outcome (with the only exception of Dr. Talc’s story). Finally, we have formulated an alternative outline of secondary plots than the ones supported by preceding research by using numeric metrics, demonstrating that “Paradise Vendors” and “Dorian Greene”, previously considered secondary storylines, exert a high impact on Confederacy’s finale, and are, perhaps, not so secondary plots after all. (shrink)

The causal impacts of genes and environment on any one biological trait are inextricably entangled, and consequently it is widely accepted that it makes no sense in singleton cases to privilege either factor for particular credit. On the other hand, at a population level it may well be the case that one of the factors is responsible for more variation than the other. Standard methodological practice in biology uses the statistical technique of analysis of variance to measure this (...) latter kind of causal efficacy. In this paper, I argue that: 1) analysis of variance is in fact badly suited to this role; and. (shrink)

Abstract In this paper I consider an easier-to-read and improved to a certain extent version of the causal chance-based analysis of counterfactuals that I proposed and argued for in my A Theory of Counterfactuals. Sections 2, 3 and 4 form Part I: In it, I survey the analysis of the core counterfactuals (in which, very roughly, the antecedent is compatible with history prior to it). In section 2 I go through the three main aspects of this (...) class='Hi'>analysis, which are the following. First, it is a causal analysis, in that it requires that intermediate events to which the antecedent event is not a cause be preserved in the main truth-condition schema. Second, it highlights the central notion to the semantics of counterfactuals on the account presented here -- the notion of the counterfactual probability of a given counterfactual, which is the probability of the consequent given the following: the antecedent, the prior history, and the preserved intermediate events. Third, it considers the truth conditions for counterfactuals of this sort as consisting in this counterfactual probability being higher than a threshold. In section 3, I re-formulate the analysis of preservational counterfactuals in terms of the notion of being a cause, which ends up being quite compact. In section 4 I illustrate this analysis by showing how it handles two examples that have been considered puzzling – Morgenbesser's counterfactual and Edgington's counterfactual. Sections 5 and on constitute Part II: Its main initial thrust is provided in section 5, where I present the main lines of the extension of the theory from the core counterfactuals (analyzed in part I) to counterfactuals (roughly) whose antecedents are not compatible with their prior history. In this part II, I elaborate on counterfactuals that don't belong to the core, and more specifically on so-called reconstructional counterfactuals (as opposed to the preservational counterfactuals, which constitute the core counterfactual-type). The heart of the analysis is formulated in terms of processes leading to the antecedent (event/state), and more specifically in terms of processes likely to have led to the antecedent, a notion which is analyzed entirely in terms of chance. It covers so-called reconstructional counterfactuals as opposed to the core, so-called preservational counterfactuals, which are analyzed in sections 2 and 3 of part I. The counterfactual probability of such reconstructional counterfactuals is determined via the probability of possible processes leading to the antecedent weighed, primarily and roughly, by the conditional probability of the antecedent given such process: The counterfactual probability is thus, very roughly, a weighted sum for all processes most likely to have led to the antecedent, diverging at a fixed time. In section 6 I explain and elaborate further on the main points in section 5. In section 7 I illustrate the reconstructional analysis. I specify counterfactuals which are so-called process-pointers, since their consequent specifies stages in processes likely to have led to their antecedent. I argue that so-called backtracking counterfactuals are process-pointers counterfactuals, which fit into the reconstructional analysis, and do not call for a separate reading. I then illustrate cases where a speaker unwittingly employs a certain counterfactual while charitably construable as intending to assert (or ‘having in mind’) another. Here I also cover the issue of how to construe what one can take as back-tracking counterfactuals, or counterfactuals of the reconstructional sort, and more specifically, which divergence point they should be taken as alluding to (prior to which the history is held fixed). Some such cases also give rise to what one can take as a dual reading of a counterfactual between preservational and reconstructional readings. Such cases may yield an ambiguity, where in many cases one construal is dominant. In section 8 I illustrate the analysis by applying it to the famous Bizet-Verdi counterfactuals. This detailed analysis of counterfactuals (designed for the indeterministic case) has three main distinctive elements: its being chance-based, its causal aspect, and the use it makes of processes most likely to have led to the antecedent-event. This analysis is couched in a very different conceptual base from, and is an alternative account to, analyses in terms of the standard notion of closeness or distance of possible worlds, which is the main feature of the Stalnaker-Lewis-type analyses of counterfactuals. This notion of closeness or distance plays no role whatsoever in the analysis presented here. (This notion of closeness has been left open by Stalnaker, and to significant extent also by Lewis's second account.) . (shrink)

In a recent paper Causal Asymmetry, Douglas Ehring has proposed an intriguing solution to the vexing problem of causal asymmetry. The aim of this paper is to show that his theory is not satisfactory. Moreover, the examples that I use in showing the defect of Ehring's theory also indicate that the counterfactual analysis of causation has a problem that cannot be remedied by Marshall Swain's suggested refinement of the counterfactual analysis of causation in Causation and Distinct (...) Events. (shrink)

One of Stuart Glennan's most prominent contributions to the new mechanist debate consists in his reductive analysis of higher-level causation in terms of mechanisms (Glennan, 1996). In this paper I employ the causal Bayes net framework to reconstruct his analysis. This allows for specifying general assumptions which have to be satis ed to get Glennan's approach working. I show that once these assumptions are in place, they imply (against the background of the causal Bayes net machinery) (...) that higher-level causation indeed reduces to interactions between component parts of mechanisms. I also briefly discuss the plausibility of these assumptions and some consequences for the mechanism debate. (shrink)

In this paper we develop an analysis of dispositions by means of causal Bayes nets. In particular, we analyze dispositions as cause-effect structures that increase the probability of the manifestation when the stimulus is brought about by intervention in certain circumstances. We then highlight several advantages of our analysis and how it can handle problems arising for classical analyses of dispositions such as masks, mimickers, and finks.

Randomized controlled trials are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population. Having at hand a sample of the target population of interest allows us to generalize the causal effect. Identifying the treatment effect in the target population requires covariates to capture all treatment effect modifiers that are shifted between the two sets. Standard estimators then use either (...) weighting, outcome modeling, or combine the two in doubly robust approaches. However, such covariates are often not available in both sets. In this article, after proving L 1 {L}^{1} -consistency of these three estimators, we compute the expected bias induced by a missing covariate, assuming a Gaussian distribution, a continuous outcome, and a semi-parametric model. Under this setting, we perform a sensitivity analysis for each missing covariate pattern and compute the sign of the expected bias. We also show that there is no gain in linearly imputing a partially unobserved covariate. Finally, we study the substitution of a missing covariate by a proxy. We illustrate all these results on simulations, as well as semi-synthetic benchmarks using data from the Tennessee student/teacher achievement ratio, and a real-world example from critical care medicine. (shrink)

Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed to quantify deviation from conditional exchangeability, given measured confounders. These sensitivity parameters are combined with the observed data to produce a “bias-corrected” estimate of the causal effect of interest. We provide important generalizations of these sensitivity analyses, by allowing for arbitrary exposures (...) and a wide range of different causal effect measures, through the specification of the target causal effect as a parameter in a generalized linear model with the arbitrary link function. We show how our generalized sensitivity analysis can be easily implemented with standard software, and how its sensitivity parameters can be calibrated against measured confounders. We demonstrate our sensitivity analysis with an application to publicly available data from a cohort study of behavior patterns and coronary heart disease. (shrink)