# Subjective Causality

arXiv: https://arxiv.org/abs/2401.10937 · HTML: https://mlsanity.com/paper/2401.10937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Language-based Decisions](https://mlsanity.com/paper/2106.11494/) (2021)
- [Sequential Language-based Decisions](https://mlsanity.com/paper/2307.07563/) (2023)

## Built on by (most cited first, 1 of 1)

- [World Models in Pieces: Structural Certification for General Agents](https://mlsanity.com/paper/2606.24842/) (2026)

## Abstract (arXiv)

We show that it is possible to understand and identify a decision maker's subjective causal judgements by observing her preferences over interventions. Following Pearl [2000], we represent causality using causal models (also called structural equations models), where the world is described by a collection of variables, related by equations. We show that if a preference relation over interventions satisfies certain axioms (related to standard axioms regarding counterfactuals), then we can define (i) a causal model, (ii) a probability capturing the decision-maker's uncertainty regarding the external factors in the world and (iii) a utility on outcomes such that each intervention is associated with an expected utility and such that intervention A is preferred to B iff the expected utility of A is greater than that of B. In addition, we characterize when the causal model is unique. Thus, our results allow a modeler to test the hypothesis that a decision maker's preferences are consistent with some causal model and to identify causal judgements from observed behavior.