The Causal Frame Problem: An Algorithmic Perspective

TL;DR

This paper introduces a novel framework called PLIF that models how the mind efficiently focuses on relevant causal information using Potential Level, offering insights into the causal frame problem and aligning with psychological findings.

Contribution

It proposes the PL-based Inference Framework (PLIF) for bounded rationality in causal reasoning, integrating causal Bayes nets and psychological insights at the algorithmic level.

Findings

PLIF aligns with existing causal judgment research

PL predicts how time is encoded in causal reasoning

Framework supports bounded rationality in causal inference

Abstract

The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ability to make decisions on the basis only of what is relevant to an ongoing situation without having explicitly to consider all that is not relevant?" In this work, we focus on the causal variant of the FP, the Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can be (implicitly) represented by a causal Bayes net, we first introduce a notion called Potential Level (PL). PL, in essence, encodes the relative position of a node with respect to its neighbors in a causal Bayes net. Drawing on the psychological literature on causal judgment, we substantiate the claim that PL may bear on how time is encoded in the mind. Using PL, we propose an inference framework, called the PL-based Inference Framework (PLIF), which permits a boundedly-rational approach to the CFP to be formally articulated at Marr's algorithmic level of analysis. We show that our proposed framework, PLIF, is consistent with a wide range of findings in causal judgment literature, and that PL and PLIF make a number of predictions, some of which are already supported by existing findings.