The role of causal models in multiple judgments under uncertainty

Hayes, Brett K.; Hawkins, Guy E.; Newell, Ben R.; Pasqualino, Martina; Rehder, Bob

doi:10.1016/j.cognition.2014.08.011

Cited by 22 publications

(17 citation statements)

References 42 publications

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“…Cohen & Staub, 2015). Both the current work and previous studies (Hayes et al, 2014;Rottman & Hastie, 2014;Waldmann, 2007) show that causal information can alter people's interpretation of the components of problems involving intuitive statistical estimation. However only when such change in problem representation is accompanied by mathematical expertise are we likely to see any improvement in normative accuracy.…”

Section: Discussionsupporting

confidence: 62%

“…On the positive side, there have been clear demonstrations of how the causal framing of key statistics can alter people's interpretation of judgment problems. Hayes, Hawkins, Newell, Pasqualino, and Rehder (2014), for example, found that when the false positive rate in mammogram problems had no obvious cause, it was treated as a stochastic variable, such that the observation of multiple false positives across successive tests was seen as highly unlikely. In contrast, when false positives were attributed to a specific cause (benign cyst), the probability of false positives across successive mammogram tests was seen as relatively stable.…”

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confidence: 99%

“…First, empirical work carried out since Krynski and Tenenbaum (2007) has found inconsistent evidence for the claim that causal explanation improves judgment accuracy (cf. Hayes et al, 2014;McNair & Feeney, 2014. Second, when causal facilitation of performance has been observed, there has been no direct evidence that this is due to increased use of Bayesian reasoning.…”

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confidence: 99%

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Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way

et al. 2017

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Three studies reexamined the claim that clarifying the causal origin of key statistics can increase normative performance on Bayesian problems involving judgment under uncertainty. Experiments 1 and 2 found that causal explanation did not increase the rate of normative solutions. However, certain types of causal explanation did lead to a reduction in the magnitude of errors in probability estimation. This effect was most pronounced when problem statistics were expressed in percentage formats. Experiment 3 used process-tracing methods to examine the impact of causal explanation of false positives on solution strategies. Changes in probability estimation following causal explanation were the result of a mixture of individual reasoning strategies, including non-Bayesian mechanisms, such as increased attention to explained statistics and approximations of subcomponents of Bayes' rule. The results show that although causal explanation of statistics can affect the way that a problem is mentally represented, this does not necessarily lead to an increased rate of normative responding.

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Section: Discussionsupporting

confidence: 62%

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Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way

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“…There are connections between the current emphasis on posterior intuitive estimate and work showing that knowledge of causal relations can influence probabilistic reasoning (e.g., Ajzen, 1977;Hayes, Hawkins, Newell, Pasqualino, & Rehder, 2014;Krynski & Tenenbaum, 2007;McNair & Feeney, 2015;Tversky & Kahneman, 1980). For example, telling participants that the presence of a cyst can cause a positive mammogram can improve performance on the standard medical diagnosis problem described above.…”

Section: Discussionmentioning

confidence: 99%

Beliefs and Bayesian reasoning

2016

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We examine whether judgments of posterior probabilities in Bayesian reasoning problems are affected by reasoners' beliefs about corresponding real-world probabilities. In an internet-based task, participants were asked to determine the probability that a hypothesis is true (posterior probability, e.g., a person has a disease, given a positive medical test) based on relevant probabilities (e.g., that any person has the disease and the true and false positive rates of the test). We varied whether the correct posterior probability was close to, or far from, independent intuitive estimates of the corresponding 'real-world' probability. Responses were substantially closer to the correct posterior when this value was close to the intuitive estimate. A model in which the response is a weighted sum of the intuitive estimate and an additive combination of the probabilities provides an excellent account of the results.

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“…This definition implicitly operates over particular representations: discrete states, such as events or facts that have some probability of occurring or being true. Because of this, experimental work in causal cognition has primarily focused on causal relationships between discrete valued (often binary) variables (e.g., Sloman, 2005;Krynski and Tenenbaum, 2007;Ali et al, 2011;Fernbach and Erb, 2013;Hayes et al, 2014;Rehder, 2014;Rothe et al, 2018). These are typically presented in contexts in which temporal information is either unavailable or abstracted away so that cases can be summarized in a contingency table.…”

Section: Probabilistic Causation Over Discrete Eventsmentioning

confidence: 99%

Causal Structure Learning in Continuous Systems

2020

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Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e., those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with continuous dynamic systems, systems that include continuous variables that interact over time (and that can be continuously observed in real time by the learner). To explore such systems, we develop a new framework that represents a causal system as a network of stationary Gauss-Markov ("Ornstein-Uhlenbeck") processes and show how such OU networks can express complex dynamic phenomena, such as feedback loops and oscillations. To assess adult's abilities to learn such systems, we conducted an experiment in which participants were asked to identify the causal relationships of a number of OU networks, potentially carrying out multiple, temporally-extended interventions. We compared their judgments to a normative model for learning OU networks as well as a range of alternative and heuristic learning models from the literature. We found that, although participants exhibited substantial learning of such systems, they committed certain systematic errors. These successes and failures were best accounted for by a model that describes people as focusing on pairs of variables, rather than evaluating the evidence with respect to the full space of possible structural models. We argue that our approach provides both a principled framework for exploring the space of dynamic learning environments as well as new algorithmic insights into how people interact successfully with a continuous causal world.

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The role of causal models in multiple judgments under uncertainty

Cited by 22 publications

References 42 publications

Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way

Causal explanation improves judgment under uncertainty, but rarely in a Bayesian way

Beliefs and Bayesian reasoning

Causal Structure Learning in Continuous Systems

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