Investigating interference or spillover effects among units is a central task in many social science problems. Network experiments are powerful tools for this task, which avoids endogeneity by ...
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 ...
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Daniel E. Ho, Kosuke Imai, Gary King, and Elizabeth A. Stuart, Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference, 15 Political Analysis 199 (2007).
A large-scale empirical study shows that hyperparameter tuning, not the choice of algorithm, is the dominant factor in ...
Every large language model begins as a question of proportion. How much web text? How much code? How much scientific literature? The ratio of training data across these domains — what researchers call ...
Causal AI is a form of artificial intelligence (AI) designed to identify and understand the cause and effect of relationships across data. Unlike large language models and generative AI, which are ...
Yılmaz, Övünç; Son, Yoonseock; Shang, Guangzhi; Arslan, Hayri A. Causal inference under selection on observables in operations management research: Matching methods and synthetic controls. Journal of ...
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