CausaLM: Causal Model Explanation Through Counterfactual Language Models

Running head: CAUSAL AND COUNTERFACTUAL EXPLANATION Mental Simulation and the Nexus of Causal and Counterfactual Explanation

From Causal Models To Counterfactual Structures

Galles and Pearl [1998] claimed that “for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis’s [possible-worlds] framework.” This claim is examined carefully, with the goal of clarifying the exact relationship between causal models and Lewis’s framework. Recursive models are shown to correspond precisely to a subclass of ...

Causal Explanation from Mathematical Models

Decision support systems may require the use of existing complex mathematical models. It is desirable to reduce the equations of such a model to an explanatory causal form to support decision making, for example in making decisions about the greenhouse effect. This paper presents an approach to constructing causal explanation from existing complex mathematical models. The method combines inform...

Generating Simplest Models for Causal Explanation

Central to most scientific and engineering tasks design, diagnosis, analogy and design rationale capture are descriptions of a device that capture its salient features with respect to how it works . These descriptions are used as the central focus of many engineering reasoning techniques . Yet little has been said about what constitutes a best, or even adequate, description . Likewise, related ...

Counterfactual Language Model Adaptation for Suggesting Phrases

Mobile devices use language models to suggest words and phrases for use in text entry. Traditional language models are based on contextual word frequency in a static corpus of text. However, certain types of phrases, when offered to writers as suggestions, may be systematically chosen more often than their frequency would predict. In this paper, we propose the task of generating suggestions tha...