# Causal Conditionals, Tendency Causal Claims and Statistical Relevance

## TL;DR

This paper compares how people judge tendency causal claims and conditional statements, focusing on how probability influences these judgments.

## Contribution

It introduces a comparative empirical study on the probabilistic factors affecting causal and conditional judgments.

## Key findings

- Judgments on tendency causal claims and conditionals are influenced by probabilistic factors.
- Conditional probability and statistical relevance have different predictive power for causal and conditional claims.
- The study reveals how these factors drive differences in human reasoning.

## Abstract

Indicative conditionals and tendency causal claims are closely related (e.g., Frosch and Byrne, 2012), but despite these connections, they are usually studied separately. A unifying framework could consist in their dependence on probabilistic factors such as high conditional probability and statistical relevance (e.g., Adams, 1975; Eells, 1991; Douven, 2008, 2015). This paper presents a comparative empirical study on differences between judgments on tendency causal claims and indicative conditionals, how these judgments are driven by probabilistic factors, and how these factors differ in their predictive power for both causal and conditional claims.