# Virtual Room 2: Causal Inference

## Date:

Wednesday, July 15, 2020, 12:00pm to 1:30pm

### Chair: Kenichi Ariga (University of Toronto)

Co-Host: Ilayda Onder (Penn State University)

## Casual Inference, or How I Learned to Stop Worrying and Love Hypothesis Testing

### Discussant: Luke Keele (University of Pennsylvania)

Many social scientists now consider it necessary for an empirical research design to achieve identification of a causal relationship as defined by the Rubin (1974) causal model or the closely related Pearl (2009) model. In this paper, we argue that no empirical estimand can take a meaningful causal interpretation without a supporting theoretical structure, even if that estimand is strongly identified by a careful research design; that is, an identified research design is necessary but not sufficient for a causal inference. An atheoretical estimand might be causal'' in the narrow sense that changes in the dependent variable are ascribable to the treatment in the specific data used in the study, but not in the sense of providing predictive or explanatory guidance for treatment effects in any other situation in the past, present, or future. For instance, when objects of study strategically interact with one another the straightforward application of common causal inference research designs will yield misleading results. To summarize our argument, there can be NO CAUSATION WITHOUT EXPLANATION.