Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...
Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
Machine learning algorithms are widely used for decision making in societally high-stakes settings from child welfare and criminal justice to healthcare and consumer lending. Recent history has ...
The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
Recent years have witnessed an increased interest, both in statistics and in the social sciences, in time-dependent models as a vehicle for the causal interpretation of series of events. The Humean ...
Social scientists are interested in the effects of low-dimensional latent treatments within texts, such as the effect of an attack on a candidate in a political advertisement. We provide a framework ...
This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...
Gow, Ian D., David F. Larcker, and Peter C. Reiss. "Causal Inference in Accounting Research." Journal of Accounting Research 54, no. 2 (May 2016): 477–523.
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