# A Non-Interventionist Approach to Causal Reasoning based on Lewisian Counterfactuals

arXiv: https://arxiv.org/abs/2505.12972 · HTML: https://mlsanity.com/paper/2505.12972/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning.
- Builds on 0 of its 44 references that are in the map.
- First posted on arXiv 19 May 2025.

## Placed in

- LLM reasoning / [Causal reasoning in LLMs](https://mlsanity.com/lr/causal-reasoning-in-llms/)

## Built on by (most cited first, 2 of 2)

- [Learning Half-Spaces from Perturbed Contrastive Examples](https://mlsanity.com/paper/2602.02080/) (2026)
- [On the Semantics of Primary Cause in Hybrid Dynamic Domains](https://mlsanity.com/paper/2602.14994/) (2026)

## Abstract (arXiv)

We present a computationally grounded semantics for counterfactual conditionals in which i) the state in a model is decomposed into two elements: a propositional valuation and a causal base in propositional form that represents the causal information available at the state; and ii) the comparative similarity relation between states is computed from the states' two components. We show that, by means of our semantics, we can elegantly formalize the notion of actual cause without recurring to the primitive notion of intervention. Furthermore, we provide a succinct formulation of the model checking problem for a language of counterfactual conditionals in our semantics. We show that this problem is PSPACE-complete and provide a reduction of it into QBF that can be used for automatic verification of causal properties.