scholarly journals Causal judgments approximate the effectiveness of future interventions

2018 ◽  
Author(s):  
Adam Morris ◽  
Jonathan Scott Phillips ◽  
Thomas Icard ◽  
Joshua Knobe ◽  
Tobias Gerstenberg ◽  
...  
Keyword(s):  
Causal Judgment ◽  
Causal Judgments

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.

Rules of Causal Judgment

2017 ◽  
Author(s):  
José C. Perales ◽  
Andrés Catena ◽  
Antonio Cándido ◽  
Antonio Maldonado
Keyword(s):  
Real Life ◽  
Statistical Information ◽  
Causal Judgment ◽  
Psychological Processes ◽  
Relevant Evidence ◽  
Clear Winner ◽  
Causal Judgments ◽  
The Media

Our environment is rich in statistical information. Frequencies and proportions—or their visual depictions—are pervasive in the media, and frequently used to support or weaken causal statements, or to bias people’s beliefs in a given direction. The topic of this chapter is how people integrate naturally available frequencies and probabilities into judgments of the strength of the link between a candidate cause and an effect. We review studies investigating various rules that have been claimed to underlie intuitive causal judgments. Given that none of these rules has been established as a clear winner, we conclude presenting a tentative framework describing the general psychological processes operating when people select, ponder, and integrate pieces of causally-relevant evidence with the goal of meeting real-life demands.

scholarly journals Norms Affect Prospective Causal Judgments

2019 ◽  
Author(s):  
Paul Henne ◽  
Kevin O'Neill ◽  
Paul Bello ◽  
Sangeet Khemlani ◽  
Felipe De Brigard
Keyword(s):  
Selection Effect ◽  
Causal Judgment ◽  
Selection Effects ◽  
Causal Judgments

People more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.

scholarly journals A Counterfactual Explanation for the Action Effect

2019 ◽  
Author(s):  
Paul Henne ◽  
Laura Niemi ◽  
Angel Pinillos ◽  
Felipe De Brigard ◽  
Joshua Knobe
Keyword(s):  
Causal Judgment ◽  
Action Effect ◽  
Original Action ◽  
Causal Judgments

People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse action effect, in which people judge inactions to be more causal than actions in overdetermination cases. Experiment 2 directly compares the two effects in joint-causation and overdetermination scenarios and conceptually replicates them with new scenarios. Taken together, these studies provide support for the new counterfactual explanation for the action effect in causal judgment.

scholarly journals An interaction effect of norm violations on causal judgment

2021 ◽  
Author(s):  
Maureen Gill ◽  
Jonathan F. Kominsky ◽  
Thomas Icard ◽  
Joshua Knobe
Keyword(s):  
Interaction Effect ◽  
Causal Judgment ◽  
Interaction Pattern ◽  
Moral Norms ◽  
Norm Violations ◽  
Proper Functioning ◽  
Causal Judgments ◽  
Robust Evidence

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interaction pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N = 1000) and norms of proper functioning (N = 3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.

scholarly journals Quantitative causal selection patterns in token causation

2019 ◽  
Author(s):  
Adam Morris ◽  
Jonathan Scott Phillips ◽  
Tobias Gerstenberg ◽  
Fiery Andrews Cushman
Keyword(s):  
Good Reason ◽  
Causal Judgment ◽  
Lightning Strike ◽  
Lightning Strikes ◽  
Prior Probabilities ◽  
Causal Judgments ◽  
Causal Selection

When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air -- in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people's causal judgments are on a continuum -- and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people's causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories.

scholarly journals A counterfactual simulation model of causal judgment

2020 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Noah D. Goodman ◽  
David Lagnado ◽  
Joshua Tenenbaum
Keyword(s):  
Simulation Model ◽  
Causal Judgment ◽  
Physical Domain ◽  
Heuristic Model ◽  
Physical Support ◽  
Quantitative Mapping ◽  
Causal Judgments ◽  
Counterfactual Simulation

How do people make causal judgments? We introduce the counterfactual simulation model (CSM) which predicts causal judgments by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in three experiments in which participants make causal judgments about dynamic collision events. Experiment 1 establishes a very close quantitative mapping between causal judgments and counterfactual simulations. Experiment 2 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where 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, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain.

scholarly journals Certainty and Singular Causal Judgment

2021 ◽  
Author(s):  
Kevin O'Neill
Keyword(s):  
Decision Making ◽  
Causal Judgment ◽  
Metacognitive Judgments ◽  
Formal Framework ◽  
Wide Range ◽  
Causal Judgments ◽  
Causal Strength

In this paper, I map out broad aims, challenges, predictions, and implica- tions for the resulting intersection of singular causal judgment and metacognition that I (tentatively) call causal metacognition. First, I will overview research on sin- gular causal judgment, focusing on popular counterfactual theories that provide a formal framework for evaluating dependency relationships, as well as several compet- ing definitions of singular causal strength. Next, I will provide relevant background in the literature on metacognition for perception and decision-making, discussing major computational theories of metacognitive judgments. After covering the small amount of work on uncertainty in causal judgments, I will then argue that although singular causal judgments pose a particular problem for some theories of metacognition, coun- terfactual theories of singular causal judgment already provide testable predictions for confidence in causal judgments and can be extended to account for a wide range of patterns in confidence in singular causal judgments. Finally, I will summarize why we need a study of causal metacognition, and what empirical and theoretical advancements in that field might look like.

Tests of the Power PC Theory of Causal Induction with Negative Contingencies

2002 ◽  
Vol 49 (2) ◽  
pp. 81-88 ◽  
Author(s):  
David R. Shanks
Keyword(s):  
Base Rate ◽  
Causal Judgment ◽  
Constant Power ◽  
Causal Induction ◽  
Cause And Effect ◽  
Causal Judgments

Abstract. The power PC theory of causal induction ( Cheng, 1997 ) proposes that causal estimates are based on the power p of a potential cause, where p is the contingency between the cause and effect normalized by the base rate of the effect. Previous tests of this theory have concentrated on generative causes that have positive contingencies with their associated outcomes. Here we empirically test this theory in two experiments using preventive causes that have negative contingencies for their outcomes. Contrary to the power PC theory, the results show that causal judgments vary with contingency across conditions of constant power p. This pattern is consistent, however, with several alternative accounts of causal judgment.

scholarly journals Normality and actual causal strength

2017 ◽  
Author(s):  
Thomas Icard ◽  
Jonathan F. Kominsky ◽  
Joshua Knobe
Keyword(s):  
Causal Models ◽  
Causal Judgment ◽  
Actual Causation ◽  
Probabilistic Sampling ◽  
Causal Judgments ◽  
Causal Strength

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation.

Stimulus structure and causal judgments

1997 ◽  
Author(s):  
Albert F. Smith ◽  
Nadine M. Meyers ◽  
Portia T. Rivera
Keyword(s):  
Causal Judgments
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