9 papers
Algorithmic Feature Highlighting for Human-AI Decision-Making
Yifan Guo, Jann Spiess
Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In s…
Causal Effect Estimation with Latent Textual Treatments
Omri Feldman, Amar Venugopal, Jann Spiess +1
Understanding the causal effects of text on downstream outcomes is a central task in many applications. Estimating such effects requires researchers to run controlled experiments t…
Testing Monotonicity in a Finite Population
Jiafeng Chen, Jonathan Roth, Jann Spiess
We consider the extent to which we can learn from a completely randomized experiment whether all individuals have treatment effects that are weakly of the same sign, a condition we…
Algorithmic Assistance with Recommendation-Dependent Preferences
Bryce McLaughlin, Jann Spiess
When an algorithm provides risk assessments, we typically think of them as helpful inputs to human decisions, such as when risk scores are presented to judges or doctors. However,…
Causal Inference on Outcomes Learned from Text
Iman Modarressi, Jann Spiess, Amar Venugopal
We propose a machine-learning tool that yields causal inference on text in randomized trials. Based on a simple econometric framework in which text may capture outcomes of interest…
Designing Algorithmic Recommendations to Achieve Human-AI Complementarity
Bryce McLaughlin, Jann Spiess
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly mo…