Observing and Intervening: Rational and Heuristic Models of Causal Decision Making

Abstract

Abstract:Recently, a number of rational theories have been put forward which provide a coherent formal framework for modeling different types of causal inferences, such as prediction, diagnosis, and action planning. A hallmark of these theories is their capacity to simultaneously express probability distributions under observational and interventional scenarios, thereby rendering it possible to derive precise predictions about interventions ("doing") from passive observations ("seeing"). In Part 1 of the paper we discuss different modeling approaches for formally representing interventions and review the empirical evidence on how humans draw causal inferences based on observations or interventions. We contrast deterministic interventions with imperfect actions yielding unreliable or unknown outcomes. In Part 2, we discuss alternative strategies for making interventional decisions when the causal structure is unknown to the agent. A Bayesian approach of rational causal inference, which aims to infer the structure and its parameters from the available data, provides the benchmark model. This account is contrasted with a heuristic approach which knows categories of causes and effects but neglects further structural information. The results of computer simulations show that despite its computational parsimony the heuristic approach achieves very good performance compared to the Bayesian model.

The Influence of Causal Knowledge in Two-Alternative Forced-Choice Tasks

“…In line with other authors [7,10,60], we hypothesize that people do not process all possible cues in their natural environments but rather use their causal knowledge-i.e., their knowledge about causal relationships between events in the environment-to focus on a small and manageable subset of relevant cues. We further expect that causal knowledge might also aid learning of cue validities.…”

“…However, these experimental results on the use of takethe-best need to be qualified [see also 60]. In many of these studies, participants were encouraged to use cues in the order of their validity by being informed about cue validities or the validity order [48,49,59,61].…”

“…Seen through the lens of the fast and frugal heuristics framework, causal knowledge helps people to select valid cues in the environment, which might be placed in a high position in the cue ordering, that is, in the hierarchy of cues that is accessed by the search process of a decision-making strategy (see also [60]). To the extent that the feedback about whether a decision was correct or incorrect leads to an updating of cue validities, the cue ordering might consequently be updated as well.…”

Strategies to intervene on causal systems are adaptively selected

“…For the most part, work on causal reasoning and structure learning has rarely adopted such an adaptive viewpoint on strategy selection (but see Meder, Gerstenberg, Hagmayer, & Waldmann, 2010;Rottman, 2014). Instead the focus has been to propose optimal principles of causal reasoning (e.g., Cheng, 1997;Gopnik et al, 2004;Griffiths & Tenenbaum, 2005;Rehder, 2014;Sobel, Tenenbaum, & Gopnik, 2004) to either explain people's behavior or to point out specific violations of these principles.…”

The Suppression of Inferences From Counterfactual Conditionals

“…The theory has been adapted as an account of human reasoning (e.g., Sloman & Lagnado, 2005) and can accommodate causal relations such as over‐determination (Lagnado, Gerstenberg, & Zultan, 2013). It may require additions to incorporate norms (e.g., Dehghani, Iliev, & Kaufmann, 2010) and heuristics to identify nodes as causes or effects of a target node (e.g., Meder, Gerstenberg, Hagmayer, & Waldmann, 2010).…”