Causal Reasoning, Counterfactual Reasoning, and the Consequences of Knowledge

2011 ◽  
Author(s):  
Barbara A. Spellman ◽  
Elizabeth A. Gilbert ◽  
Elizabeth R. Tenney ◽  
Christopher R. Holland
Keyword(s):  
Causal Reasoning ◽  
Counterfactual Reasoning

scholarly journals Changing Minds ― Epistemic Interventions in Causal Reasoning

2021 ◽  
Author(s):  
Lara Kirfel ◽  
David Lagnado
Keyword(s):  
Allergic Reaction ◽  
Causal Reasoning ◽  
Epistemic State ◽  
Counterfactual Reasoning ◽  
Epistemic States ◽  
Epistemic Actions ◽  
Causal Action ◽  
Epistemic Context ◽  
Future Outcomes ◽  
Causal Cognition

Did Tom’s use of nuts in the dish cause Billy’s allergic reaction? According to counterfactual theories of causation, an agent is judged a cause to the extent that their action made a difference to the outcome (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2020; Gerstenberg, Halpern, & Tenenbaum, 2015; Halpern, 2016; Hitchcock & Knobe, 2009). In this paper, we argue for the integration of epistemic states into current counterfactual accounts of causation. In the case of ignorant causal agents, we demonstrate that people’s counterfactual reasoning primarily targets the agent’s epistemic state – what the agent doesn’t know –, and their epistemic actions – what they could have done to know – rather than the agent’s actual causal action. In four experiments, we show that people’s causal judgment as well as their reasoning about alternatives is sensitive to the epistemic conditions of a causal agent: Knowledge vs. ignorance (Experiment 1), self-caused vs. externally caused ignorance (Experiment 2), the number of epistemic actions (Experiment 3), and the epistemic context (Experiment 4). We see two advantages in integrating epistemic states into causal models and counterfactual frameworks. First, assuming the intervention on indirect, epistemic causes might allow us to explain why people attribute decreased causality to ignorant vs. knowing causal agents. Moreover, causal agents’ epistemic states pick out those factors that can be controlled or manipulated in order to achieve desirable future outcomes, reflecting the forward-looking dimension of causality. We discuss our findings in the broader context of moral and causal cognition.

scholarly journals Decision Prioritization and Causal Reasoning in Decision Hierarchies

2021 ◽  
Author(s):  
Ariel Zylberberg
Keyword(s):  
Decision Tree ◽  
Human Behavior ◽  
Causal Reasoning ◽  
Probability Distributions ◽  
Real Life ◽  
Counterfactual Reasoning ◽  
Perceptual Decision Making ◽  
Human Ability ◽  
Branching Points ◽  
Human Participants

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target's location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with $10^7$ latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants' behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

scholarly journals Decision-Theoretic Foundations for Causal Reasoning

1995 ◽  
Vol 3 ◽  
pp. 405-430 ◽  
Author(s):  
D. Heckerman ◽  
R. Shachter
Keyword(s):  
Canonical Form ◽  
Special Class ◽  
Causal Reasoning ◽  
Directed Acyclic Graphs ◽  
Influence Diagrams ◽  
Counterfactual Reasoning ◽  
Cause And Effect ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Theoretic Foundations

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

scholarly journals Decision prioritization and causal reasoning in decision hierarchies

2021 ◽  
Vol 17 (12) ◽  
pp. e1009688
Author(s):  
Ariel Zylberberg
Keyword(s):  
Decision Tree ◽  
Human Behavior ◽  
Causal Reasoning ◽  
Probability Distributions ◽  
Real Life ◽  
Counterfactual Reasoning ◽  
Perceptual Decision Making ◽  
Human Ability ◽  
Branching Points ◽  
Human Participants

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

The Sympathetic Process and Judgments of Propriety

2017 ◽  
Author(s):  
Eric Schliesser
Keyword(s):  
Causal Reasoning ◽  
Moral Luck ◽  
Counterfactual Reasoning ◽  
Causal Order ◽  
Causal Relations ◽  
The Third

This chapter describes Adam Smith’s views on sympathy and sympathetic judgment(s). It shows that the sympathetic process presupposes and crucially depends on counterfactual, causal reasoning. In particular the chapter argue for four related claims. The first is that according to Smith the sympathetic process depends on a type of causal reasoning that goes well beyond the kind of simulationist theory standardly attributed to him. The second is that the Smithian imagination in the sympathetic process works by way of counterfactual reasoning and that even the feelings we ought to feel as a consequence of the sympathetic process need not be actual, but counterfactual. The third is that Smithian agents are non-trivially understood as belonging to the causal order of nature. This chapter illustrates this third point through an extended digression on Smith’s views on moral luck (the piacular). Fourth, Smithian judgments of propriety are intrinsically judgments about the proportionality of causal relations.

Further explorations of causal reasoning in autoshaping

2013 ◽  
Author(s):  
Robert I. Bowers ◽  
William D. Timberlake
Keyword(s):  
Causal Reasoning
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