Language and causal inference

The focus of my research is to understand how linguistic expressions can elicit causal representations in people and how those representations allow them to make appropriate judgements of events in the world. This investigation tackles both ends of communication, production and interpretation, through the lens of causal inference. Underpinning this investigation, is a need to understand the class of causal representations that people may be using when making or interpreting causal judgements. In other words, I seek to understand not only how people reason from their causal representations but also what determines likely causal representations they are using to make inferences. Thus, the challenge of my research is to jointly identify the possible representational and inferential mechanisms in place during communication. I primarily aim to investigate this through the use of psycholinguistic experiments evaluating the production and interpretation of causal judgements. Specific tasks include the production and interpretation of causal selection explanation as well as counterfactual explanations. Utilizing computational models of semantic interpretation and judgement-making that are based on causal models, we can empirically validate appropriate mechanisms necessary for these linguistic tasks. Meanwhile, I aim to extend these computational models identified in the empirical studies to further evaluate theoretical implications of such mechanisms. One approach is to evaluate the information-theoretic value and computational cost of causal representations relative to the linguistic tasks of interest. With this, I hope to motivate the need to understand the semantics of causal language that helps us collectively make inferences and better understand the world.