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Theory-based causal induction
- In
, 2003
"... Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various s ..."
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Cited by 56 (19 self)
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Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.
Representing causation
- Journal of Experiment Psychology: General
, 2007
"... The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and expl ..."
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Cited by 42 (5 self)
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The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.
Bayesian generic priors for causal learning
- Psychological Review
, 2008
"... The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high ..."
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Cited by 34 (5 self)
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The article presents a Bayesian model of causal learning that incorporates generic priors—systematic assumptions about abstract properties of a system of cause–effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes—causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.
Erlbaum Associates
- User Centered System Design: New Perspectives on Human-Computer Interaction
, 1986
"... Storing and restoring visual input with collaborative rank coding and ..."
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Cited by 32 (0 self)
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Storing and restoring visual input with collaborative rank coding and
Time as guide to cause
"... How do people learn causal structure? In two studies we investigated the interplay between temporal order, intervention and covariational cues. In Study 1 temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2 both te ..."
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Cited by 24 (2 self)
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How do people learn causal structure? In two studies we investigated the interplay between temporal order, intervention and covariational cues. In Study 1 temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2 both temporal order and intervention contributed to accurate causal inference, well beyond that achievable through covariational data alone. Together the studies show that people use both temporal order and interventional cues to infer causal structure, and that these cues dominate the available statistical information. We endorse a hypothesis-driven account of learning, whereby people use cues such as temporal order to generate initial models, and then test these models against the incoming covariational data.
Analogical and category-based inference: A theoretical integration with Bayesian causal models
- Journal of Experimental Psychology: General
, 2010
"... A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sen ..."
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Cited by 10 (1 self)
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A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source information at various levels of abstraction (including both specific instances and more general categories), coupled with prior causal knowledge, to build a causal model for a target situation, which in turn constrains inferences about the target. We propose a computational theory in the framework of Bayesian inference and test its predictions (parameter-free for the cases we consider) in a series of experiments in which people were asked to assess the probabilities of various causal predictions and attributions about a target on the basis of source knowledge about generative and preventive causes. The theory proved successful in accounting for systematic patterns of judgments about interrelated types of causal inferences, including evidence that analogical inferences are partially dissociable from overall mapping quality.
Causal structure learning over time: Observations and interventions.
, 2012
"... a b s t r a c t Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent -the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X ..."
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Cited by 9 (1 self)
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a b s t r a c t Seven studies examined how people learn causal relationships in scenarios when the variables are temporally dependent -the states of variables are stable over time. When people intervene on X, and Y subsequently changes state compared to before the intervention, people infer that X influences Y. This strategy allows people to learn causal structures quickly and reliably when variables are temporally stable (Experiments 1 and 2). People use this strategy even when the cover story suggests that the trials are independent (Experiment 3). When observing variables over time, people believe that when a cause changes state, its effects likely change state, but an effect may change state due to an exogenous influence in which case its observed cause may not change state at the same time. People used this strategy to learn the direction of causal relations and a wide variety of causal structures (Experiments 4-6). Finally, considering exogenous influences responsible for the observed changes facilitates learning causal directionality (Experiment 7). Temporal reasoning may be the norm rather than the exception for causal learning and may reflect the way most events are experienced naturalistically.
Heuristics in Covariation-based Induction of Causal Models: Sufficiency and Necessity Priors
"... Our main goal in the present set of studies was to re-visit the question whether people are capable of inducing causal models from covariation data alone without further cues, such as temporal order. In the literature there has been a debate between bottom-up and top-down learning theories in causal ..."
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Cited by 5 (2 self)
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Our main goal in the present set of studies was to re-visit the question whether people are capable of inducing causal models from covariation data alone without further cues, such as temporal order. In the literature there has been a debate between bottom-up and top-down learning theories in causal learning. Whereas top-down theorists claim that in structure induction, covariation information plays none or only a secondary role, bottom-up theories, such as causal Bayes net theory, assert that people are capable of inducing structure from conditional dependence and independence information alone. Our three experiments suggest that both positions are wrong. In simple three-variable domains people are indeed often capable of reliably picking the right model. However, this can be achieved by simple heuristics that do not require complex statistics.
Learning Causal Direction from Repeated Observations over Time
"... Inferring the direction of causal relationships is notoriously difficult. We propose a new strategy for learning causal direction when observing states of variables over time. When a cause changes state, its effects will likely change, but if an effect changes state due to an exogenous factor, its o ..."
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Cited by 4 (4 self)
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Inferring the direction of causal relationships is notoriously difficult. We propose a new strategy for learning causal direction when observing states of variables over time. When a cause changes state, its effects will likely change, but if an effect changes state due to an exogenous factor, its observed cause will likely stay the same. In two experiments, we found that people use this strategy to infer whether X→Y vs. X←Y, and X→Y→Z vs. X←Y→Z produced a set of data. We explore a rational Bayesian and a heuristic model to explain these results and discuss implications for causal learning.
Causal learning through repeated decision making
- Proceedings of the 30th Annual Conference of the Cognitive Science Society 2008
"... Abstract Many decisions refer to actions that have a causal impact on other events. Such actions allow for mere learning of expected values, but also for causal learning about the structure of the decision context. Whereas most theories of decision making neglect causal knowledge, causal learning t ..."
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Cited by 3 (2 self)
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Abstract Many decisions refer to actions that have a causal impact on other events. Such actions allow for mere learning of expected values, but also for causal learning about the structure of the decision context. Whereas most theories of decision making neglect causal knowledge, causal learning theories emphasize the importance of causal beliefs and assume that people represent decision problems in terms of their causal structure. In three studies we investigated the representations people acquire when repeatedly making decisions to maximize a certain payoff. Our results show that (i) initial causal hypotheses guide the interpretation of decision feedback, (ii) consequences of interventions are used to revise existing causal beliefs, (iii) decision makers use the experienced feedback to induce a causal model of the choice situation, which (iv) enables them to adapt their choices to changes of the decision problem.
