Causal Intuitions Research Articles

Why are socioeconomic health inequalities unacceptable? Studying the influence of explanatory framings on cognitive appraisals

AbstractStudies of aversion to health inequality have found that this is often greater when health outcomes are presented as varying with socioeconomic conditions. We sought to understand better why this is by studying the cognitive appraisals made about health inequality when presented with distinct explanatory framings. Across two pre‐registered studies (N = 1321), UK and US participants judged the acceptability of life expectancy differences attributed to distinct framings: income, education, social class, neighborhood, lifestyle choices, and genetics. Health inequality was least acceptable when attributed to the four socioeconomic framings, and most acceptable for lifestyle choices and genetics. Six appraisal dimensions—complexity, malleability, inevitability, and extent driven by biological, psychological, and sociocultural causes—varied with framing and predicted views on health inequality. These dimensions could explain most of the drop in acceptability for health inequality attributed to socioeconomic factors relative to a condition with no framing. This work illustrates for the first time the cognitive appraisals and causal intuitions that link different explanatory framings to views on health inequality. These framings are viewed as least acceptable because they reduce the perceived involvement of biological causes while increasing the perception that sociocultural and psychological factors contribute to health inequality.Public significance statement: Academics use different socioeconomic variables to describe health inequalities. We show that different explanatory framings change appraisals about the causes, malleability, and inevitability of health inequalities. Socioeconomic explanations (income, education, social class, and neighborhood) reduce acceptability because they reduce the perception that health is biologically caused and increase the perceived role of socio‐cultural factors. Public support for intervention on health inequalities will be best served by framings that emphasize these differences

Learners' causal intuitions explain behavior in control of variables tasks.

Self-directed learners are described as "intuitive scientists," yet they often struggle in assessments of their scientific reasoning skills. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry behavior. Specifically, we consider whether learners' documented failure to correctly apply the control of variables strategy might stem from a mismatch between task presentation and their intuitions as causal learners. In Experiment 1, children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that was modified to clarify ambiguous elements of the causal logic in the original design. In all age groups, a significant majority of participants selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. In Experiment 2, we replicate these results with real-world stimuli, and in Experiments 3 and 4, we provide direct evidence that self-directed learners apply specific causal intuitions to experimentation tasks. Together, these findings support a novel alternative interpretation of the apparently paradoxical gap between learners' success in informal exploration and their error-prone experimentation-both behaviors are consistent with an intuitively causal approach to scientific inquiry. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Causal Opacity or Causal Translucence?

According to longstanding interpretations in the social and cognitive sciences, rituals are said to be characterized by arbitrary action and the lack of a causal connection between action and desired outcome. The observer who assigns a physical-causal connection has taken the instrumental stance, while one who accepts a group convention is said to take the ritual stance. I argue that in religious rituals at least, including those with magical elements, the gap is bridged and causal intuitions are present, if limited. For example, we rely on a mental heuristic called Representativeness in order to make many causal judgments, and Representativeness tells us that effects usually resemble their causes. This heuristic, studied by Daniel Kahnemann and Amos Tversky, corresponds to J. G. Frazer’s so-called “Law of Similarity” in magic. Representativeness and other forms of magical thinking appear to yield weaker causal inferences than our intuitions about physical processes or the agency of other people. Accordingly, religious rituals are often employed in situations where a goal cannot be achieved in more obvious ways, but some lesser intuition of causal efficacy can still be generated. Illustrative examples are drawn from ancient Greek rituals of offering, oath-taking, and purification.

Counterfactuals and the logic of causal selection.

Everything that happens has a multitude of causes, but people make causal judgments effortlessly. How do people select one particular cause (e.g., the lightning bolt that set the forest ablaze) out of the set of factors that contributed to the event (the oxygen in the air, the dry weather … )? Cognitive scientists have suggested that people make causal judgments about an event by simulating alternative ways things could have happened. We argue that this counterfactual theory explains many features of human causal intuitions, given two simple assumptions. First, people tend to imagine counterfactual possibilities that are both a priori likely and similar to what actually happened. Second, people judge that a factor C caused effect E if C and E are highly correlated across these counterfactual possibilities. In a reanalysis of existing empirical data, and a set of new experiments, we find that this theory uniquely accounts for people's causal intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Causal intuition in the indefinite world: Meta-analysis and simulations

Modeling our causal intuition can contribute to understanding our behavior. In this paper, we introduce a causal induction model called proportion of assumed-to-be rare instances (pARIs) and examine its adaptive properties. We employ the two-stage theory of causal induction proposed by Hattori and Oaksford in 2007, which divides causal induction into two stages: first, observed events are sifted and likely candidates are extracted; second, each of them is verified through intervention. Here, we focus on the first stage. We conducted a meta-analysis and computer simulations in a similar way to Hattori and Oaksford (2007) but with some corrections and improvements. We added two experiments and excluded one in our reconstructed meta-analysis and augmented the simulations by correcting two problems. Our meta-analysis results show that pARIs outperforms more than 40 existing models in terms of data fit from human causal induction experiments while being simpler. Additionally, our simulation results show that pARIs outperforms DFH in terms of population covariation detection, especially under small sample sizes and rarity of events. Overall, pARIs qualifies as one of the best models for the first stage of causal induction. These findings may enable a deeper understanding of our cognitive biases. The first stage can now be considered a causal discovery stage where the topology of causal models is to be hypothesized.

Why Your Causal Intuitions are Corrupt: Intermediate and Enabling Variables

When evaluating theories of causation, intuitions should not play a decisive role, not even intuitions in flawlessly-designed thought experiments. Indeed, no coherent theory of causation can respect the typical person’s intuitions in redundancy (pre-emption) thought experiments, without disrespecting their intuitions in threat-and-saviour (switching/short-circuit) thought experiments. I provide a deductively sound argument for these claims. Amazingly, this argument assumes absolutely nothing about the nature of causation. I also provide a second argument, whose conclusion is even stronger: the typical person’s causal intuitions are thoroughly unreliable. This argument proceeds by raising the neglected question: in what respects is information about intermediate and enabling variables relevant to reliable causal judgment?

A New Contact Paradox

There is a well-known variety of contact paradoxes which are significantly linked to topology. The aim of this paper is to present a new paradox concerning contact with bodies composed of a denumerable infinity of parts. This paradox establishes the logical necessity, in a Newtonian context, of contact forces (herein called “phantom forces”) that violate what is probably our most basic causal intuition, embodied in what I call the Principle of Influence: any force exerted on a body B induces (causes) change of movement of B or (inclusive disjunction) the emergence of internal forces in B. However, the above paradox can be made strictly compatible with a Newtonian framework by introducing phantom forces as ideal elements in the Hilbert sense, though it will be seen that this does not solve all the problems.

Why Causation is Biconditional but not Proportional

Abstract In this article, I defend a biconditional counterfactual account of causation, which places equal emphasis on what I call “the presence condition” and “the absence condition,” whereas Lewis's classical counterfactual theory focuses only on the absence condition. I attempt to show that biconditionalism provides a promising treatment of supervenient causation, namely, causal cases involving the supervenience relationship. Although some philosophers confuse this account with the proportionality constraint on causation, I argue that biconditionalism is distinct from and superior to proportionalism in accommodating our reliable causal intuitions.

Minimal Theory of Causation and Causal Distinctions

The Minimal Theory of Causation, presented in Graßhoff and May, 2001, aspires to be a version of a regularity analysis of causation able to correctly predict our causal intuitions. In my article, I will argue that it is unsuccessful in this respect. The second aim of the paper will be to defend Hitchcock’s proposal concerning divisions of causal relations (presented in Hitchcock, 2001) against criticism made, in Jakob, 2006 on the basis of the Minimal Theory of Causation.

Causal Intuition and Delayed-Choice Experiments.

The conventional explanation of delayed-choice experiments appears to violate our causal intuition at the quantum level. I reanalyze these experiments using time-reversed and time-symmetric formulations of quantum mechanics. The time-reversed formulation does not give the same experimental predictions. The time-symmetric formulation gives the same experimental predictions but actually violates our causal intuition at the quantum level. I explore the reasons why our causal intuition may be wrong at the quantum level, suggest how conventional causation might be recovered in the classical limit, propose a quantum analog to the classical block universe viewpoint, and speculate on implications of the time-symmetric formulation for cosmological boundary conditions.

Pre-emption cases may support, not undermine, the counterfactual theory of causation

Pre-emption cases have been taken by almost everyone to imply the unviability of the simple counterfactual theory of causation. Yet there is ample motivation from scientific practice to endorse a simple version of the theory if we can. There is a way in which a simple counterfactual theory, at least if understood contrastively, can be supported even while acknowledging that intuition goes firmly against it in pre-emption cases – or rather, only in some of those cases. For I present several new pre-emption cases in which causal intuition does not go against the counterfactual theory, a fact that has been verified experimentally. I suggest an account of framing effects that can square the circle. Crucially, this account offers hope of theoretical salvation – but only to the counterfactual theory of causation, not to others. Again, there is (admittedly only preliminary) experimental support for this account.

Normal Causes for Normal Effects: Reinvigorating the Correspondence Hypothesis About Judgments of Actual Causation

There have been several recent attempts to model ordinary intuitions about actual causation by combining a counterfactual definition of the causal relation with an abnormality-based account of causal judgments. In these models, the underlying psychological theory is that people automatically focus on abnormal events when judging the actual causes of an effect. This approach has enabled authors such as Halpern and Hitchcock (Br J Philos Sci axt050, 2014) to capture an impressive array of ordinary causal intuitions. However, in this paper I demonstrate how these abnormality-based accounts still systematically fail to predict ordinary causal judgments in specific types of scenarios: those in which the effect is normal. I will argue that the reason for this is that the underlying psychological theory is wrong: the idea that intuitive actual causes are abnormal events is only partially correct. To model ordinary judgments more realistically, researchers working in this area must adopt a more plausible underlying psychological theory: the correspondence hypothesis about judgments of actual causation. One of the consequences of this correspondence hypothesis is that normal effects are judged to have normal causes.

Interpreting Heritability Causally

A high heritability estimate usually corresponds to a situation in which trait variation is largely caused by genetic variation. However, in some cases of gene-environment covariance, causal intuitions about the sources of trait difference can vary, leading experts to disagree as to how the heritability estimate should be interpreted. We argue that the source of contention for these cases is an inconsistency in the interpretation of the concepts ‘genotype’, ‘phenotype’, and ‘environment’. We propose an interpretation of these terms under which trait variance initially caused by genetic variance is subsumed into a heritability for all cases of gene-environment covariance.

Heritability and causal reasoning

Gene–environment (G–E) covariance is the phenomenon whereby genetic differences bias variation in developmental environment, and is particularly problematic for assigning genetic and environmental causation in a heritability analysis. The interpretation of these cases has differed amongst biologists and philosophers, leading some to reject the utility of heritability estimates altogether. This paper examines the factors that influence causal reasoning when G–E covariance is present, leading to interpretive disagreement between scholars. It argues that the causal intuitions elicited are influenced by concepts of agency and blame-worthiness, and are intimately tied with the conceptual understanding of the phenotype under investigation. By considering a phenotype-specific approach, I provide an account as to why causal ascriptions can differ depending on the interpreter. Phenotypes like intelligence, which have been the primary focus of this debate, are more likely to spark disagreement for the interpretation of G–E covariance cases because the concept and ideas about its ‘normal development’ relatively ill-defined and are a subject of debate. I contend that philosophical disagreement about causal attributions in G–E covariance cases are in essence disagreements regarding how a phenotype should be defined and understood. This moves the debate from one of an ontological flavour concerning objective causal claims, to one concerning the conceptual, normative and semantic dependencies.

Russellian Monism and Epiphenomenalism

AbstractContemporaries often reject epiphenomenalism (EPI) out of hand, while Russellian Monism (RM) is regarded as worthy of further development. It is argued here that this difference of attitudes is indefensible, because the easy rejection of EPI is due to its violating a certain Causal Intuition, and RM implicitly violates that same intuition. An enriched version of RM mitigates the violation, but the same mitigation results if we make a parallel enrichment of EPI. If RM and EPI are approached on a level playing field, it is not obvious which will prove to be the better view.

Efficacy of a causal value function in game tree search

Classical search methods on game trees are based on a static evaluation function (that enable quantitative valuation of game states) and a decision strategy (such as the minimax method). These search methods are not always effective in some games such as the game of Go, as construction of the evaluation function is very hard and the search space is extremely huge. Recently, Monte Carlo tree search methods (especially the UCT algorithms) that enable efficient sampling of actions have been shown to be very effective. Here, we propose the loosely symmetric (LS) model applied to trees (LST), which utilises an action value function (LS model) that implements causal intuition of humans. By tuning a single intuitive parameter, LST enables fast search of the optimal action with its efficient satisficing behaviour. The satisficing search realised by LST enables pruning and exhibits intermediate properties between those of breadth-first and depth-first search strategies.

Retrocausality at no extra cost

One obstacle faced by proposals of retrocausal influences in quantum mechanics is the perceived high conceptual cost of making such a proposal. I assemble here a metaphysical picture consistent with the possibility of retrocausality and not precluded by the known physical structure of our reality. This picture employs two relatively well-established positions—the block universe model of time and the interventionist account of causation—and requires the dismantling of our ordinary asymmetric causal intuition and our ordinary intuition about epistemic access to the past. The picture is then built upon an existing model of agent deliberation that permits us to strike a harmony between our causal intuitions and the fixity of the block universe view. I conclude that given the right mix of these reasonable metaphysical and epistemological ingredients there is no conceptual cost to such a retrocausal picture of quantum mechanics.

The landscape of causation

Paul and Ned Hall's book makes an original and important contribution to the philosophical debate on causation. Their aim is not to construct a theory of causation but to sketch a map of the landscape (p. 1) constituted by a rich set of problem cases and various theories of causation devised to account for them. Chapter 1 presents the scope and aim of the book, justifies the method of evaluating theories of causation by exploring whether they are refuted by counterexamples, and provides an overview of the rest of the book. Chapter 2 justifies the choice of problems and solutions considered in the book. Several assumptions are made from the beginning. Causation is taken to be a relation among particular events rather than types of events. A reductionist outlook is adopted, according to which facts about what causes what are determined by i) facts about what happens and ii) fundamental laws. These assumptions provide the background for a minimal sufficiency account of causation (p. 16), which is a version of a regularity account. The other approaches that are introduced and examined in light of their capacity to cope with problem cases include the following: probabilistic accounts, transference accounts, and counterfactual accounts. Several versions of the counterfactual account get special attention: 1) Lewis' (1973) theory of chains of non-­‐ backtracking counterfactual dependence, 2) Lewis' (2000) account of causation as influence, 3) accounts of de facto dependence , versions of which have been put forward by Yablo (2004) and Hitchcock (2001), and finally 4) contrastivism, according to which causal relations have the form C rather than C* causes E rather than E* . But Paul and Hall provide more than a useful overview of the literature. They analyze the aims that the various approaches fix themselves or, in the authors' own words, the conflicting motivations and conflicting presuppositions about the very point of providing a philosophical account of causation (p. 1). Their own conception of giving an account of causation is expressed in a list of rules. An account of causation is satisfactory if 1) it is reductive and avoids circular use of causal notions in the analysis, 2) it is metaphysically conservative and avoids the postulation of extravagant entities, such as negative events to account for causation by omission, 3) it is conceptually parsimonious and tries to analyze causation as directly as possible in terms of fundamental categories, 4) it applies not only to our actual world but also to other possible worlds, 5) it leaves open the possibility that some causal intuitions are wrong. Chapters 3 to 5 evaluate in rigorous detail how several approaches, the most promising ones according to Paul and Hall, cope with problem cases. Chapter 3 explores various types of redundant causation , such as preemption and overdetermination, which have been the focus of an important part of the literature on counterfactual approaches to causation. Chapters 4 and 5 explore causation by omission and two types of situation in which it is unclear whether causation is transitive, i.e. double prevention and switching. The last chapter, Chapter 6 contains concluding remarks.

Causal Responsibility in International Criminal Law

This article clarifies the factual basis for attributing causal responsibility in interpersonal causation scenarios in international criminal law. Such content is obliterated when explaining causal contributions in overdetermined and indeterministic harm scenarios. The resulting gap between individual agency and causal attribution is explained away by reference to values underlying legal liability (‘causal minimalism’). Probabilistic causal models can express causal influences in interpersonal causation scenarios, and explicate the objective basis of causal attribution. International Criminal Courts’ approaches to indirect and joint perpetration, as well as the notion of causal contribution in joint criminal enterprise are discussed in light of the existing approaches to testing causation in law, as well as with regard to Judea Pearl’s notion of causal sustenance. The article concludes that expressing causal contributions in language of probabilities can explain causal intuitions underlying legal liability better than supplanting factual basis of attributing responsibility with normative and policy justifications.

Cognitively inspired reinforcement learning architecture and its application to giant-swing motion control

Many algorithms and methods in artificial intelligence or machine learning were inspired by human cognition. As a mechanism to handle the exploration–exploitation dilemma in reinforcement learning, the loosely symmetric (LS) value function that models causal intuition of humans was proposed (Shinohara et al., 2007). While LS shows the highest correlation with causal induction by humans, it has been reported that it effectively works in multi-armed bandit problems that form the simplest class of tasks representing the dilemma. However, the scope of application of LS was limited to the reinforcement learning problems that have K actions with only one state (K-armed bandit problems). This study proposes LS-Q learning architecture that can deal with general reinforcement learning tasks with multiple states and delayed reward. We tested the learning performance of the new architecture in giant-swing robot motion learning, where uncertainty and unknown-ness of the environment is huge. In the test, the help of ready-made internal models or functional approximation of the state space were not given. The simulations showed that while the ordinary Q-learning agent does not reach giant-swing motion because of stagnant loops (local optima with low rewards), LS-Q escapes such loops and acquires giant-swing. It is confirmed that the smaller number of states is, in other words, the more coarse-grained the division of states and the more incomplete the state observation is, the better LS-Q performs in comparison with Q-learning. We also showed that the high performance of LS-Q depends comparatively little on parameter tuning and learning time. This suggests that the proposed method inspired by human cognition works adaptively in real environments.