Business/policy decisions are often based on evidence from randomized experiments and observational studies. In this article we propose an empirical framework to estimate the value of evidence-based decision making (EBDM) and the return on the investment in statistical precision.

This talk lays out a straightforward framework for treatment effect inference with image-valuate control covariates, and numeric treatments and outcomes, a setting that is increasingly more common as image data becomes more available. The proposed method combines predictions from deep convolutional neural networks with semiparametric de-biasing…

Inattentive and distracted respondents are increasingly a concern for survey researchers. The failure of respondents to pay attention to questions and treatments introduces noise into data sets, weakening correlations between items and increasing the likelihood of null findings. Various methods have been proposed to identify inattentive…

Consumer choices are increasingly mediated by algorithms, which use data on those past choices to infer consumer preferences and then curate future choice sets. Behavioral economics suggests one reason these algorithms so often fail: choices can systematically deviate from preferences. For example, research shows that prejudice can arise not…