[gov3009-l] Applied Statistics 9/14/16 - Tobias Gerstenberg

Ban, Pamela pban at fas.harvard.edu
Mon Sep 12 10:18:35 EDT 2016

Dear all,

This week at the Applied Statistics Workshop we will be welcoming Tobias Gerstenberg, a postdoctoral fellow at MIT.  He will be presenting work entitled "A Counterfactual Simulation Model of Causal Judgment."  Please find the abstract below and on the website<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home>.

We will meet in CGIS Knafel Room 354 at noon and lunch will be provided.  See you all there!


Title: A Counterfactual Simulation Model of Causal Judgment

Abstract: How do we make causal judgments? In this talk, I will present a counterfactual simulation model (CSM) of causal judgments that unifies different philosophical views on causation. The CSM predicts that people's causal judgments are influenced by the extent to which a candidate cause made a difference to i) whether the outcome occurred, and ii) how it occurred. I will show how whether-causation and how-causation can be expressed in terms of different counterfactual contrasts defined over the same generative model of a domain. I will focus on applying the CSM to the domain of intuitive physics, asking people to make judgments about colliding billiard balls. The CSM accounts for participants' causal judgments to a high degree of quantitative accuracy. Causal judgments increased the more certain participants were that a ball was a whether-cause, a how-cause, as well as sufficient for bringing about the outcome. The CSM postulates that people make causal judgments by comparing what actually happened with what would have happened if the candidate cause had been removed from the scene. In direct support of this claim, I will show eye-tracking data of how people mentally simulate how the counterfactual world would have unfolded. I will conclude by discussing how the CSM may help us better understand the mapping between causal events in the world and the words we use to describe them.

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