Stable Causal Relationships Are Better Causal Relationships

Vasilyeva, Nadya; Blanchard, Thomas; Lombrozo, Tania

doi:10.1111/cogs.12605

Cited by 39 publications

(51 citation statements)

References 40 publications

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“…robust-causation. Both philosophers (Lewis, 1986;Woodward, 2006) and psychologists (Lombrozo, 2010;Vasilyeva, Blanchard, & Lombrozo, 2018) have argued that another important aspect of causal relationships is their robustness. Causal relationships are robust to the extent that they would have continued to hold even if the background conditions had been different.…”

Section: Consider the Diagram Inmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

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How do people make causal judgments? We propose the counterfactual simulation model (CSM) of causal judgment which predicts causal judgments by comparing what actually happened with counterfactual simulations of what would have happened in relevant contingencies. It postulates different aspects of causation that capture whether the cause made a difference to whether and how the outcome occurred, and if the cause was sufficient and robust. We test the CSM in three experiments that ask participants to make judgments about dynamic collision events. Experiment 1 reveals a very close quantitative mapping between causal judgments, and participants’ belief that the outcome would have been different without the cause. Experiment 2 demonstrates that counterfactual contrasts are necessary for explaining causal judgments. Participants’ judgments differ dramatically between pairs of situations in which what actually happened was identical, but what would have happened differed. Experiment 3 features two candidate causes and shows that participants’ judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants’ judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, deal with causation by omission, and be extended beyond the physical domain.

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Section: Consider the Diagram Inmentioning

confidence: 99%

A counterfactual simulation model of causal judgments for physical events

Goodman¹,

Lagnado²,

Tenenbaum³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In particular, thinking of interventions as actions a person could take to alter her environment has been proffered as a natural explanation for the existence of causal selection-that is, why people consider some causes more causal than others. If, in general, causes are variables which can be intervened on to manipulate effects, then perhaps the causes foregrounded by causal selection are particularly good interventions-ones that would work well as actions across a variety of situations (Hitchcock, 2012;Lombrozo, 2010;Vasilyeva, Blanchard, & Lombrozo, 2016;Woodward, 2006).…”

Section: Relation To Existing Interventionist Approachesmentioning

confidence: 99%

“…likely develop a representation of the "general" effectiveness of interventions, averaging across situation-specific details (more formally, marginalizing over all variables V = E ∈ W ). Other authors have proposed a similar idea-that a useful representation of good interventions should be "robust" or "portable", rather than being tethered to a highly specific state of affairs (Franklin & Frank, 2018;Hitchcock, 2012;Lombrozo, 2010;Vasilyeva et al, 2016). For instance, a person might know that lighting a match is, in most situations, an effective way to start a fire-even if there are some rare situations where it is ineffective (e.g., in strong winds).…”

Section: General Effectiveness As a Decision Prior For This Reason Dmentioning

confidence: 99%

“…For instance, if two soldiers simultaneously shoot a prisoner in the head, neither was necessary in the actual circumstances-but the first soldier would have been necessary had the second soldier not pulled the trigger (Halpern & Hitchcock, 2011;Halpern & Pearl, 2005;Hitchcock, 2001;Lucas & Kemp, 2015 Both of these approaches-the sufficiency analysis and the counterfactual necessity analysis-serve the same basic function as the SAMPLE model. By using a generative model of the situation to infer what would have happened in plausible altered circumstances, people may be generating additional "observations" of the causal system (Blanchard, Vasilyeva, & Lombrozo, 2017;Vasilyeva et al, 2016). Exploring these and other accounts of causal judgment in overdetermination and preemption remains an important area for further research.…”

Section: Descriptive Boundary Condition: Cases Of Overdetermination Amentioning

confidence: 99%

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Causal judgments approximate the effectiveness of future interventions

Morris¹,

Phillips²,

Icard³

et al. 2018

Preprint

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When many things contributed to an outcome, people consistently judge certain ones to be more causal than others. For instance, people believe that a fire was more caused by the lit match than by the surrounding oxygen that fueled it. Why? Here, we offer a functional account of such patterns in causal judgment: By selecting causes as people naturally do, repeated judgments of whether something (e.g. the match) was the cause of an outcome (e.g. the fire) can be averaged to obtain the probability that introducing those things would produce the outcome (e.g., that lighting a match would start a fire). In other words, token causal judgments accumulate evidence about the general effectiveness of potential future interventions. We offer a formal account of this process, and show how it explains three basic qualitative features of causal judgment: why the causes people select tend (1) to be necessary, (2) to be abnormal, and (3) to lack abnormal counterparts.

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“…Empirical evidence supporting interventionism's role for invariance in our causal thinking comes primarily from studies looking at highly similar concepts, such as 'explanation generality' (e.g., Friedman, 1974;Gelman, Star, & Flukes, 2002;Johnston, Sheskin, Johnson, & Keil, 2018;Kitcher, 1981;Strevens, 2009;Walker, Lombrozo, Legare, & Gopnik, 2014). Very recently, computational (Morris et al, 2018) and behavioral (Vasilyeva et al, 2018) studies have begun to look explicitly at interventionist invariance and find that it both reflects and influences our causal judgments.…”

Section: Causal Invariance and Interventionismmentioning

confidence: 99%

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Lapidow

Walker

2020

Language and Concept Acquisition From Infancy Through Childhood

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To appear in Childers, J.B., Graham, S.A. & Namy, L. (Eds). (2019). Learning Language and Concepts from Multiple Examples in Infancy and Childhood What is invariant does not emerge unequivocally except with a flux. The essentials become evident in the context of changing nonessentials.-James Gibson, 1979 Human learners are intuitively exploratory: We acquire new knowledge from the outcomes of our actions. However, in order for exploration to support learning, at least some of these actions must serve to evaluate our existing knowledge. Despite this need for informative 'hypothesis testing' in everyday learning, decades of research examining self-directed experimentation suggests that learners rarely choose informative tests. That is, instead of selecting actions to test whether their current hypothesis is correct, both children and adults tend to prefer 'positive tests': actions that will produce an effect assuming their current hypothesis is correct (see Klayman, 1995; Zimmerman, 2007). To illustrate, suppose you drop an ice cube on the floor and it shatters. As a learner, you might form an initial hypothesis that 'impact with an unyielding surface causes ice to shatter.' This hypothesis is also a causal explanation for your observation: indicating how one variable (X) makes a difference to the state of another variable (Y). According to traditional interpretations of Popper's (1959) falsificationist approach, testing this hypothesis would require disconfirming its alternatives. That is, assessing whether 'X is the cause of Y,' requires 'negative tests,' or actions to determine whether Y occurs in the absence of X. Here, since Y is 'shattering' and X is 'impacting an unyielding surface,' you should drop an ice cube on a yielding surface (not-X), like rubber or cotton, to determine whether it will shatter. However, learners rarely choose this kind of disconfirming action during their exploration. Instead, they are much more likely to repeat the initial observation: e.g., to pick up another ice-cube and drop it on the same surface, or a similar one. This tendency to generate multiple positive examples is a puzzling characteristic of self-directed learning, since it does not initially appear to be informative. After all, these repeated demonstrations often produce the same evidence, and do not distinguish between the current hypothesis (i.e., impacting an unyielding surface) and potential alternatives (e.g., impacting any surface at a particular speed), since they are consistent with both. Why then, do self-directed learners consistently and repeatedly conduct positive tests? In this chapter, we propose a novel answer to this question: the Search for Invarinace (SI) hypothesis, which suggests that observing muliple, positive examples may facilitate learning by allowing us to assess the invariance of our causal theories. That is, by repeatedly activating a hypothesized cause and checking if its anticipated effect occurs, positive tests generate information about the degree to which this relationship holds across tim...

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Stable Causal Relationships Are Better Causal Relationships

Cited by 39 publications

References 40 publications

A counterfactual simulation model of causal judgments for physical events

A counterfactual simulation model of causal judgments for physical events

Causal judgments approximate the effectiveness of future interventions

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

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