@article{TEXTUAL,
      recid = {5096},
      author = {Heckman, James J. and Pinto, Rodrigo},
      title = {Causal Inference of Social Experiments Using Orthogonal  Designs},
      journal = {Journal of Quantitative Economics},
      address = {2022-09-12},
      number = {TEXTUAL},
      abstract = {Orthogonal arrays are a powerful class of experimental  designs that has been widely used to determine efficient  arrangements of treatment factors in randomized controlled  trials. Despite its popularity, the method is seldom used  in social sciences. Social experiments must cope with  randomization compromises such as noncompliance that often  prevent the use of elaborate designs. We present a novel  application of orthogonal designs that addresses the  particular challenges arising in social experiments. We  characterize the identification of counterfactual variables  as a finite mixture problem in which choice incentives,  rather than treatment factors, are randomly assigned. We  show that the causal inference generated by an orthogonal  array of incentives greatly outperforms a traditional  design.},
      url = {http://knowledge.uchicago.edu/record/5096},
}